[0001] The present disclosure relates, in general, to a device for diagnosis of diseases, and more specifically, relates to a means for detecting and predicting early signs of ophthalmic diseases.

BACKGROUND
[0002] Background description includes information that may be useful in understanding the present disclosure. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed disclosure, or that any publication specifically or implicitly referenced is prior art.
[0003] Diabetic Retinopathy (DR) is an eye disease which occurs due to diabetes. It damages the small blood vessels in the retina resulting in loss of vision. The risk of the disease increases with age and therefore, middle aged and older diabetics are prone to diabetic retinopathy. Conventionally, retinopathy screening is done by fund us examination by ophthalmologists or with the help of colour fund us photography using conventional fund us cameras (mydriatic or non-mydriatic) by trained eye technicians or optometrists. The primary issue is the grading of the retinal images by ophthalmologists (retinal specialists) or trained persons, whose numbers are very scarce compared to the load of patients requiring screening.
[0004] Some of these patients are based in rural areas and cannot visit an eye care provider. Consecutive follow ups are required for years together, the attitude, and/or behavioural aspects negatively impact the patients practice despite knowledge of consequences. In addition to that, another major issue is that either ophthalmologist or researchers always need fund us camera which shall not be available at all the time especially in the remote place as mentioned above.
[0005] Therefore, in view of the above issues, it is extremely difficult to determine early detection of diabetic retinopathy without using fund us camera. In addition to that, the experts ophthalmologists or trained person may check whether the person is diabetic or not by clinical test but cannot predict accurately the person with prediabetic disorder.
[0006] Some of the means designed to capture the fund us images in order to predict the diabetic retinopathy includes smart scope. Smart scope is a digital medical camera that enables retinal imaging and eye anterior imaging with one hand-held device. Other technique known in the art includes smart simple technique, which is to obtain ocular fund us pictures using a smartphone camera and a conventional handheld indirect ophthalmos copy lens. However, these techniques are indispensable when picture documentation of optic nerve, retina, and retinal vessels is necessary.
[0007] Horus scope system incorporates high definition camera technology and offers video output to a monitor. However, all the above said devices are used to capture fund us images of retina and related part. The researchers exploit the fund us images of retina necessarily in order to early predict the diabetic retinopathy decease
[0008] Therefore, there is a need for a means to provide a cost-effective device that can predict the prediabetic or diabetic retinopathy disorders without using fund us camera and by solving the above-mentioned problem.

OBJECTS OF THE PRESENT DISCLOSURE
[0009] An object of the present disclosure relates, in general, to a device for diagnosis of diseases, and more specifically, relates to a means for detecting and predicting early signs of ophthalmic diseases.
[0010] Another object of the present disclosure is to provide a system that can diagnose the early prediction of diabetic retinopathy without using fund us camera.
[0011] Another object of the present disclosure is to provide a system that contain clinical parameters of the subject to determine the diagnosis.
[0012] Yet another object of the present disclosure is to provide a system in which the collected parameters are extracted, categorized by a learning engine to detect the diabetic or pre-diabetic disease.

SUMMARY
[0013] The present disclosure relates, in general, to a device for diagnosis of diseases, and more specifically, relates to a means for detecting and predicting early signs of ophthalmic diseases.
[0014] In an aspect, the present disclosure provides a system for predicting retinopathy in an eye of a subject, the system including: an information input unit adapted to receive data packets pertaining to one or more health attributes of the subject; a processor operatively coupled with a memory, the memory storing instructions executable by the processor to: receive, from the information input unit, the data packets of one or more health attributes of the subject; analyse, the received data packets to extract a set of parameters from the data packet; categorize, the extracted set of parameters based on matching of the extracted set of parameters with a reference set of parameters; extract, from the categorized set of parameters, a set of values for the categorized set of parameters, wherein, based on the determination of the categorized parameters and a deviation of the extracted set of values for the categorized set of parameters from a reference set of values, the processor is configured to predict a diagnosis for the eye of the subject, the diagnosis pertaining to state of retinopathy of the eye.
[0015] In an embodiment, the processor can be configured to, on detection of deviation for any of the extracted set of values for the categorized set of parameters, recommend corresponding diagnosis to counter the deviation.
[0016] In another embodiment, the processor can be operatively coupled to a display device to display the output data to the user.
[0017] In another embodiment, the processor can be operatively coupled to a learning engine, the learning engine trained to detect retinopathy of the eye of the user.
[0018] In another embodiment, the learning engine can be trained using a historical data of correlation of the extracted set of parameters of a received data packets of one or more health attributes of the subject with a diagnosis for the eye, the diagnosis pertaining to a state of retinopathy of the eye.
[0019] In another embodiment, the learning engine includes any or a combination of convolutional neural networks (CNN) and deep neural network (DNN).
[0020] In another embodiment, a memory device operatively coupled with the processor, the memory device configured to store a log of operations of the system, the log of operations comprising any or a combination of the data packets, the extracted set of parameters, the categorized set of parameters, the reference set of parameters, the extracted set of values for the categorized parameters, the reference set of values for the extracted parameters and the determined diagnosis for the eye of the user.
[0021] In another embodiment, the memory device can be a cloud storage.
[0022] In an aspect, the present disclosure provides a method for predicting retinopathy in an eye of a subject, the method including: receiving, at a computing device from an information input unit, the data packets of one or more health attributes of the subject; analysing, at the computing device, the received data packets to extract a set of parameters from the data packets; categorizing, at the computing device, the extracted set of parameters based on matching of the extracted set of parameters with a reference set of parameters; and extracting, at the computing device from the categorized set of parameters, a set of values for the extracted set of parameters ,wherein, based on the determination of the categorized parameters and a deviation of the extracted set of values for the categorized set of parameters from a reference set of values, the processor is configured to predict a diagnosis for the eye of the subject, the diagnosis pertaining to state of retinopathy of the eye.
[0023] Various objects, features, aspects, and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWINGS
[0024] The following drawings form part of the present specification and are included to further illustrate aspects of the present disclosure. The disclosure may be better understood by reference to the drawings in combination with the detailed description of the specific embodiments presented herein.
[0025] FIG. 1A and FIG. 1B illustrate exemplary representation of a system for predicting retinopathy in an eye of a subject, in accordance with an embodiment of the present disclosure.
[0001] FIG. 2 illustrates flow chart of the process for predicting retinopathy in an eye of a subject, in accordance with an embodiment of the present disclosure.
[0026] FIG. 3 illustrates an exemplary computer system in which or with which embodiments of the present invention can be utilized in accordance with embodiments of the present disclosure.

DETAILED DESCRIPTION
[0027] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.
[0028] If the specification states a component or feature “may”, “can”, “could”, or “might” be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic.
[0029] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[0030] The use of any and all examples, or exemplary language (e.g., “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non – claimed element essential to the practice of the invention.
[0031] The present disclosure relates, in general, to a device for diagnosis of diseases, and more specifically, relates to a means for detecting and predicting early signs of ophthalmic diseases. The device can diagnose the early prediction of prediabetic disease and diabetic retinopathy without the use fund us camera. The parameters of users suffering from Diabetic Type-1 or Type-2 can be collected to early predict the diabetic retinopathy for Type-1 and Type-2 patients.
[0032] FIG. 1A and FIG. 1B illustrate exemplary representation of a system for predicting retinopathy in an eye of a subject, in accordance with an embodiment of the present disclosure.
[0033] Referring to FIG. 1A and FIG.1B, the system 100 configured for detecting retinopathy, wherein diabetic and prediabetic retinopathy can be detected by observing various parameters of a user/subject 106. The system 100 includes a device 102 that includes an input device 110 (also referred to as information input unit 110, herein), a processor 112, a memory 116, a display device 118, a learning engine 120 and a cloud server 104. The input device 110 can be configured to receive one or more input data, and subsequently performs multiple operations on the received data in order to automatically identify and predict the disorder. The present disclosure relates to predict the user/subject 106 having diabetic retinopathy, pre-diabetic retinopathy, or a pre-diabetic condition, or who are pre-disposed to developing diabetes, pre-diabetes, or a pre-diabetic condition accordingly.
[0034] The input device 110 can be adapted to receive data packets pertaining to one or more health attributes of the subject. The input device 110 may comprise any type of input device that is capable of receiving or selecting one or more data. For example, the input device 110 may include one or more of imaging sensor, a scanner, an input interface, a network interface card, a receiver, an input port, a mouse, or a storage device, among other types of input devices. The input device 110 can obtain the one or more data that can be provided to a processor for further processing of the data.
[0035] The processor 112 operatively coupled with the memory 116, the memory storing instructions executable by the processor to receive, from the input unit 110, the data packets of one or more health attributes of the subject. The health attributes of the user/subject are in some way related to medical conditions, user health status, and health characteristics of the user and the like. The health attributes of the user may be obtained by performing a clinical test e.g., blood sugar test, urine, blood pressure level and the like, various other samples can also be used.
[0036] In another embodiment, received data packets can be analysed to extract a set of parameters (also referred to as clinical parameters, herein) from the data packet. The set of parameters may include triglycerides, cholesterol, protein in urine, consumption of high sugar drinks and the like. The extracted set of parameters can be categorized based on matching of the extracted set of parameters with a reference set of parameters. The reference set of parameters may include previously stored parameters. A set of values can be extracted, from the categorized set of parameters, wherein, based on the determination of the categorized parameters and a deviation of the extracted set of values for the categorized set of parameters from a reference set of values, the processor can predict a diagnosis for the eye of the subject, the diagnosis pertaining to state of retinopathy of the eye.
[0037] For instance, the set of values may indicate ranges of the clinical parameters of those patient/user, and the reference set of values may indicate the threshold range of the clinical parameters, when the range of the clinical parameters of the user deviate from the threshold range of the stored clinical parameters, the diagnosis pertaining to state of retinopathy of the eye can be predicted. The diagnosis for the state of retinopathy of the eye may include prediabetic disease or diabetic retinopathy.
[0038] In an embodiment, the input data can be obtained by the clinical test e.g., blood sugar test of the user/subject 106 to check the value of fasting (FF) and postprandial (PP). Based on the values of FF and PP and other various medical condition of the user, the clinical parameters are collected and categorized either for prediabetic disease or for diabetic retinopathy. The prediabetic disease can be determined by using various parameters such as blood pressure level, triglycerides, cholesterol, lack of enough exercise, family history of type-2 diabetic, raised stress level, smoking, alcohol consumption and consumption of high sugar drinks. Similarly, for diabetic retinopathy various parameters are collected such as diabetic type (type-1 or type-2), diabetes duration, hyper tension, body mass ratio, protein in urine, poor glycemic control (blood glucose and glycosylated haemoglobin levels, which may range from 200–500 mg/dl), anthropometric, waist circumference, cholesterol, hba1c. For instance, for each user/patient, based on one category if the parameters are of certain value, then it can be considered as prediabetic disease and based on another category, if the parameters are of another certain value then it can be considered as diabetic retinopathy.
[0039] The system may include one or more processors 112, the memory 116 configured to store executable instructions configured for execution by the one or more processors 112. The processor 112 can be operatively coupled to the input device 110 and can be configured to analyse the data e.g., parameters for the input data or other information characterizing the input data. The system 100 may perform computation of the data based on the data obtained taken at different time intervals, for example, approximately once every year or about six months.
[0040] In another embodiment, the collected parameters are cleaned and categorized by filtration techniques and features are extracted using a learning engine 120 e.g., convolutional neural networks (CNNs), deep neural network (DNN) and the like to detect the diabetic or prediabetic disease. The learning engine 120 can be operatively coupled to the processor 112. The parameters obtained are transmitted to the processor 112 operatively coupled to the input device 110. The data analysis can be performed by utilizing learning engine 120 e.g., CNNs. The learning engine 120 may include feature extraction unit, classification unit, filtration unit,and enhancement unit. The processor 112 can build and train an ensemble of learning engine 120 to accurately and automatically perform data processing to detect particular attributes of objects in a digital data, and to classify the objects according to the detected attributes. The determined diagnosis can be displayed on a display device operatively coupled to the processor.
[0041] The display device 118 can be communicatively coupled to the processor 112 from which it receives the categorized data. The display device 118 may include any type of output device that is capable of providing a signal (e.g., indicating the classification) to a human operator and/or another device. For example, the display device 118 may include one or more of a display, a speaker, a speech synthesizer, a network interface card, a transmitter, an output port, a haptic feedback device, or a storage device, among other types of display devices.
[0042] In another embodiment, the collected parameters and outcomes can be transmitted to the cloud server 104 through a wireless network 114. The analysed and segregated parameters are stored on cloud server 104 (also referred to as memory device 104, herein), and the user 106 can access the data anytime from any place. The cloud server 104 can be operatively coupled with the processor 112. The cloud server 104 can be configured to store a log of operations of the system, the log of operations including any or a combination of the one or more data, the extracted set of attributes, the reference set of attributes, the categorization of the extracted set of attributes, the extracted set of values for the extracted attributes, the reference set of values for the extracted attributes and the determined diagnosis for the eye of the user 106.Further, the data (parameters and outcomes) are store on the server and can be used for future reference.
[0043] Further, the network 114 can be a wireless network, a wired network or a combination thereof. The network can be implemented as one of the different types of networks, such as an intranet, local area network (LAN), wide area network (WAN), the internet, LTE network, Code Division Multiple Access (CDMA) or Global System for Mobile Communications (GSM) network or General Packet Radio Service (GPRS), and may also include an IEEE 802.11 (Wi-Fi) network, and the like. Further, network 108 can either be a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol(TCP/IP), Wireless Application Protocol (WAP), and the like, to communicate with one another. Further, the network can include a variety of network devices, including routers, bridges, servers, mobile computing devices, storage devices, and the like.
[0044] In an implementation, initially, the device 102 can be configured to obtain clinical parameters of the user/subject 106 to check whether the person is diabetic or not. If the person is not diabetic then the same device further investigates about the prediabetic disease. Therefore, the device 102 can predict the diabetic retinopathy and also evaluate the prediabetic disease, if the patient is suspect to diabetic in near future. Thus, the system 100 can diagnose the early prediction of diabetic retinopathy without using fund us camera. It only contains the clinical parameters of those user/subject 106.
[0045] FIG. 2 illustrates flow chart of the process for predicting retinopathy in an eye of a subject, in accordance with an embodiment of the present disclosure.
[0046] Referring to FIG. 2, the method 200 includes receiving 202, at a computing device from an information input unit, the data packets of one or more health attributes of the subject. The received data packets can be analysed 204 to extract a set of parameters from the data packets. The extracted set of parameters can be categorized 206 based on matching of the extracted set of parameters with a reference set of parameters and a set of values can be extracted 208 for the extracted set of parameters, wherein based on the determination of the categorized parameters and a deviation of the extracted set of values for the categorized set of parameters from a reference set of values, the processor 112 is configured to detect and predict a diagnosis 210 for the eye of the subject, the diagnosis pertaining to state of retinopathy of the eye.
[0047] Based on various medical condition of the user, the clinical parameters are collected and categorized either for prediabetic disease or for diabetic retinopathy. The clinical parameters encompasses all health status or other characteristics, such as, without limitation, age, race or ethnicity, gender, diastolic blood pressure and systolic blood pressure, family history, height, weight, waist and hip circumference, waist-hip ratio, body-mass index (BMI), past gestational diabetes mellitus, and resting heart rate.
[0048] The computing device can include processor that can be in communication with each of a memory, and input/output devices.The processor 112 may include a microprocessor or other devices capable of being programmed or configured to perform computations and instruction processing in accordance with the disclosure. Such other devices may include microcontrollers, digital signal processors (DSP), complex programmable logic device (CPLD), field programmable gate arrays (FPGA), application-specific assimilated circuits (ASIC), discrete gate logic, and/or other assimilated circuits, hardware or firmware in lieu of or in addition to a microprocessor.
[0049] The memory 116 includes programmable software instructions that are executed by the processor 112. The processor 112 may be embodied as a single processor or a number of processors. The processor 112 and a memory 116 may each be, for example located entirely within a single computer or other computing device. The memory 116, which enables storage of data and programs, may include random-access memory (RAM), read-only memory (ROM), flash memory and any other form of readable and writable storage medium.
[0050] FIG. 3 illustrates an exemplary computer system in which or with which embodiments of the present invention can be utilized in accordance with embodiments of the present disclosure.
[0051] As shown in FIG. 3, computer system 300 includes an external storage device 310, a bus 320, a main memory 330, a read only memory 340, a mass storage device 350, communication port 360, and a processor 370. A person skilled in the art will appreciate that computer system may include more than one processor and communication ports. Examples of processor 370 include, but are not limited to, an Intel® Itanium® or Itanium 2 processor(s), or AMD® Opteron® or Athlon MP® processor(s), Motorola® lines of processors, FortiSOC™ system on a chip processors or other future processors. Processor 370 may include various modules associated with embodiments of the present invention. Communication port 360 can be any of an RS-232 port for use with a modem-based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fibre, a serial port, a parallel port, or other existing or future ports. Communication port 360 may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which computer system connects.
[0052] Memory 330 can be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. Read only memory 340 can be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or BIOS instructions for processor 370. Mass storage 350 may be any current or future mass storage solution, which can be used to store information and/or instructions. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces), e.g. those available from Seagate (e.g., the Seagate Barracuda 7200 family) or Hitachi (e.g., the Hitachi Deskstar 7K1000), one or more optical discs, Redundant Array of Independent Disks (RAID) storage, e.g. an array of disks (e.g., SATA arrays), available from various vendors including Dot Hill Systems Corp., LaCie, Nexsan Technologies, Inc. and Enhance Technology, Inc.
[0053] Bus 320 communicatively couples processor(s) 370 with the other memory, storage, and communication blocks. Bus 320 can be, e.g. a Peripheral Component Interconnect (PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), USB or the like, for connecting expansion cards, drives and other subsystems as well as other buses, such a front side bus (FSB), which connects processor 370 to software system.
[0054] Optionally, operator and administrative interfaces, e.g. a display, keyboard, and a cursor control device, may also be coupled to bus 320 to support direct operator interaction with computer system. Other operator and administrative interfaces can be provided through network connections connected through communication port 360. External storage device 310 can be any kind of external hard-drives, floppy drives, IOMEGA® Zip Drives, Compact Disc - Read Only Memory (CD-ROM), Compact Disc - Re-Writable (CD-RW), Digital Video Disk - Read Only Memory (DVD-ROM). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system limit the scope of the present disclosure.
[0055] The present invention, in various embodiments, includes components, methods, processes, systems and/or apparatus substantially as depicted and described herein, including various embodiments, sub-combinations, and subsets thereof. Those of skill in the art will understand how to make and use the present invention after understanding the present disclosure. The present invention, in various embodiments, includes providing devices and processes in the absence of items not depicted and/or described herein or in various embodiments hereof, including in the absence of such items as may have been used in previous devices or processes, e.g. for improving performance, achieving ease and\or reducing cost of implementation.
[0056] It should be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refer to at least one of something selected from the group consisting of A, B, C … and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc. The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the appended claims.
[0057] While various embodiments of the present disclosure have been illustrated and described herein, it will be clear that the disclosure is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions, and equivalents will be apparent to those skilled in the art, without departing from the spirit and scope of the disclosure, as described in the claims.

ADVANTAGES OF THE PRESENT DISCLOSURE
[0058] The present disclosure provides a system that can diagnose the early prediction of diabetic retinopathy without using fund us camera.
[0059] The present disclosure provides a system that contain clinical parameters of the subject to determine the diagnosis effectively.
[0060] The present disclosure provides a system in which the collected parameters are extracted, categorizeed by a learning engine to detect the diabetic or pre-diabetic disease.

Claims:1. A system (100) for predicting retinopathy in an eye of a subject, the system comprising:
an information input unit (110) adapted to receive data packets pertaining to one or more health attributes of the subject;
a processor (112) operatively coupled with a memory (116), said memory storing instructions executable by the processor to:
receive, from the information input unit (110), the data packets of one or more health attributes of the subject;
analyse, the received data packets to extract a set of parameters from the data packet;
categorize, the extracted set of parameters based on matching of the extracted set of parameters with a reference set of parameters; and
extract, from the categorized set of parameters, a set of values for the categorized set of parameters,
wherein, based on the determination of the categorized parameters and a deviation of the extracted set of values for the categorized set of parameters from a reference set of values, the processor (112) is configured to predict a diagnosis for the eye of the subject, said diagnosis pertaining to state of retinopathy of the eye.
2. The system as claimed in claim 1, wherein the processor (112) is configured to, on detection of deviation for any of the extracted set of values for the categorized set of parameters, recommend corresponding diagnosis to counter the deviation.
3. The system as claimed in claim 1, wherein the processor (112) is operatively coupled to a display device (118) to display the output data to the user.
4. The system as claimed in claim 1, wherein the processor (112) is operatively coupled to a learning engine(120), the learning engine trained to detect retinopathy of the eye of the user.
5. The system as claimed in claim 5, wherein the learning engine (120) is trained using a historical data of correlation of the extracted set of parameters of a received data packets of one or more health attributes of the subject with a diagnosis for the eye, said diagnosis pertaining to a state of retinopathy of the eye.
6. The system as claimed in claim 5, wherein the learning engine (120) comprises any or a combination of convolutional neural networks (CNN) and deep neural network (DNN).
7. The system as claimed in claim 1, wherein a memory device (104) operatively coupled with the processor, the memory device configured to store a log of operations of the system, the log of operations comprising any or a combination of the data packets, the extracted set of parameters, the categorized set of parameters, the reference set of parameters, the extracted set of values for the categorized parameters, the reference set of values for the extracted parameters and the determined diagnosis for the eye of the user.
8. The system as claimed in claim 1, wherein the memory device is a cloud storage.
9. A method (200) predicting retinopathy in an eye of a subject, the method comprising:
receiving (202), at a computing device from an information input unit, the data packets of one or more health attributes of the subject;
analysing (204), at the computing device, the received data packets to extract a set of parameters from the data packets;
categorizing (206), at the computing device, the extracted set of parameters based on matching of the extracted set of parameters with a reference set of parameters; and
extracting (208), at the computing device from the categorized set of parameters, a set of values for the extracted set of parameters,
wherein, based on the determination of the categorized parameters and a deviation of the extracted set of values for the categorized set of parameters from a reference set of values, the processor is configured to detect and predict (210) a diagnosis for the eye of the subject, said diagnosis pertaining to state of retinopathy of the eye.
10. A device (102) for predicting retinopathy in an eye of a subject, the device comprising:
an information input unit (110) adapted to receive data packet pertaining to one or more health attributes of the subject;
a processor (112) operatively coupled with a memory (116), said memory storing instructions executable by the processor to:
receive, from the information input unit, the data packet of the one or more health attributes of the subject;
analyse, the received one or more data packet to extract a set of parameters from the one or more data packet;
categorize, the extracted set of parameters based on matching of the extracted set of parameters with a reference set of parameters; and
extract, from the categorized set of parameters, a set of values for the categorized set of parameters,
wherein, based on the determination of the categorized parameters and a deviation of the extracted set of values for the categorized set of parameters from a reference set of values, the processor is configured to predict a diagnosis for the eye of the subject, said diagnosis pertaining to state of retinopathy of the eye.