Claims:1. The present invention relatesartificial intelligence and machine learning based system for integrated diagnosis through ayurveda and modern medicine.

2. Artificial Intelligence and machine learning based system for integrated diagnosis through ayurveda, and modern medicine claimed in claim 1,the data were obtained from an online database from various sources. The data was also collected from registered Ayurvedic practitioner and registered medical practitioner.

3. Artificial Intelligence and machine learning based system for integrated diagnosis through ayurveda, and modern medicine claimed in claim 2,the input raw data and well-arranged data are both included in the data source component. Data acquisition is the process of moving data from various data sources to data staging.

4. Artificial Intelligence and machine learning based system for integrated diagnosis through ayurveda, and modern medicine claimed in claim 3, the Extract, Transform, and Loading (ETL) operations in the data staging component let data from a variety of sources be made acceptable for storage and analysis. A database is a repository for storing preprocessed data.

Description:Technical field of invention:

The present invention relatesartificial intelligence and machine learning based system for integrated diagnosis through ayurveda and modern medicine.

Background:

Ayurveda is an Indian traditional medicine system. It is used rural area now a days. It is played an important role since the ancient period in providing health care in developing countries. It is based on herbal medicine. Herbal plants have a great history from the ancient period that was used in the treatment of many diseases.

In India, these information of the Ayurvedic treatment has been lost due to improper documentation of the Ayurvedic medicines that were used in curing many diseases.Therefore,modern medicines of treatment system are popularised. Hence people are diverting towards Allopathy Treatment, which is fast in relief, well-equipped technologies that are used in the diagnosis of many diseases which makes the decision faster to start the treatment and well documentation is being maintained regarding the treatment of many diseases.

The pandemic of COVID-19 has turned the entire world into an immune system, the body's defense system against microorganisms that cause sickness, and other organisms that may cause due to airborne or contact.

Therefore,required a Centralized Expert System in which integration of best methodologies used in both Ayurvedic and Allopathy will be useful in fast decision making for disease treatment.

The system must also incorporate searching of herbs with a proper link, information validation, information extraction automatically from other collaborative resources and it must also incorporate user management using Artificial Intelligence(AI), Machine Learning(ML), and Deep Learning(DL) Algorithms. Thus, providing an Integra images, centralized Expert System with AI, ML, and DL on Ayurvedic and Allopathy will sure create the awareness of both Medicinal treatments, which will be useful in creating a healthy society, less expensive, fast diagnosis process, and environmentally friendly.

Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims.

The recitation of ranges of values herein is merely intended to serve as a shorth and method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context.

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.

The above information disclosed in this Background section is only for enhancement of understanding of the background of the invention and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art.

Objective of the invention

The primary object of thepresent invention isartificial intelligence and machine learning based system for integrated diagnosis through ayurveda and modern medicine.

Summary of the invention:

Accordingly following invention is artificial intelligence and machine learning based system for integrated diagnosis through ayurveda and modern medicine.

The data were obtained from an online database from various sources. The data was also collected from registered Ayurvedic practitioner and registered medical practitioner.

The questionnaires were framed in the form of google form with the data collected from different resources and given to people to answer through shared links.

These google forms which represented data was given as an input to different Machine Learning Algorithms. The analysis of doshas is done on the answers queries.

The database is created through well arrangement of components such as data source, data staging, data storage, and information delivery. The various building components necessary to construct a database.

The input raw data and well-arranged data are both included in the data source component. Data acquisition is the process of moving data from various data sources to data staging.

The Extract, Transform, and Loading (ETL) operations in the data staging component let data from a variety of sources be made acceptable for storage and analysis. A database is a repository for storing preprocessed data.

The health-related dataset is collected from different resources. The collected data is extracted and filtered with the relevant data that is required for the diagnosis. The common features such as questionnaires, extracting the features from medical reports are used during the diagnosis method of both Ayurveda and Modern Medicine is taken into consideration.

The extracted data from different resources are filtered and trained on the common features of the disease diagnosis method of both Ayurvedic and Modern Medicine.

The different Machine Learning algorithms like SVM, Ensemble Algorithm, Decision Tree, Logistic regression are used to analyze the datasets.

The trained model is tested and validated with the database. With the precision value, the accuracy is tested by both the physicians, and a recommendation is given for the prescription. It must serve as a knowledge system tool for many medical practitioners, research scholars, drug companies.

Brief description of drawings

Further clarify various aspects of some example embodiments of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. It is appreciated that these drawings depict only illustrated embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail through the use of the accompanying drawings.

In order that the advantages of the present invention will be easily understood, a detail description of the invention is discussed below in conjunction with the appended drawings, which, however, should not be considered to limit the scope of the invention to the accompanying drawings, in which:

Figure 1 shows a block diagram representation of system ofartificial intelligence and machine learning based system for integrated diagnosis through ayurveda and modern medicineaccording to the present invention.

Detailed description of invention:

The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the invention as defined in the claims.

In any embodiment described herein, the open-ended terms "comprising," "comprises,” and the like (which are synonymous with "including," "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of," consists essentially of," and the like or the respective closed phrases "consisting of," "consists of, the like.

The present invention relates artificial intelligence and machine learning based system for integrated diagnosis through ayurveda and modern medicine.

Ayurveda is an Indian traditional medicine system. It is used rural area now a days. It is played an important role since the ancient period in providing health care in developing countries. It is based on herbal medicine. Herbal plants have a great history from the ancient period that was used in the treatment of many diseases.

The data were obtained from an online database from various sources. The data was also collected from registered Ayurvedic practitioner and registered medical practitioner.

The questionnaires were framed in the form of google form with the data collected from different resources and given to people to answer through shared links.

Thesegoogle forms which represented data was given as an input to different Machine Learning Algorithms. The analysis of doshas is done on the answers queries.

The database is created through well arrangement of components such as data source, data staging, data storage, and information delivery. The various building components necessary to construct a database.

The input raw data and well-arranged data are both included in the data source component. Data acquisition is the process of moving data from various data sources to data staging.

The Extract, Transform, and Loading (ETL) operations in the data staging component let data from a variety of sources be made acceptable for storage and analysis. A database is a repository for storing preprocessed data.

The health-related dataset is collected from different resources. The collected data is extracted and filtered with the relevant data that is required for the diagnosis. The common features such as questionnaires, extracting the features from medical reports are used during the diagnosis method of both Ayurveda and Modern Medicine is taken into consideration.

The extracted data from different resources are filtered and trained on the common features of the disease diagnosis method of both Ayurvedic and Modern Medicine.

The different Machine Learning algorithms like SVM, Ensemble Algorithm, Decision Tree, Logistic regression are used to analyze the datasets.

The trained model is tested and validated with the database. With the precision value, the accuracy is tested by both the physicians, and a recommendation is given for the prescription. It must serve as a knowledge system tool for many medical practitioners, research scholars, drug companies.

The advantage of integrating allopathy with the Ayurvedic medicines by choosing their pros and cons in their respective fields with the application of best technology like AI, ML, and Deep Learning, which gives the best information to start the treatment fast and more accurate.

The modern technologies are needed in the medical field to explore the data and to use it in an effectively and an efficient way for the treatment of a particular disease.

Integration of both medications from Ayurvedic and Modern medicine must involve using the best possible smart treatment based on the patient’s individual condition.

Integration should be analyzed with the technologies like Artificial Intelligence, Machine Learning, and Deep Learning that give the best medicine to treat a disease.

The integration should be successful from both Ayurvedic and Modern Medicine which should take care of patient safety, the most cost-effective approach and best treatment.

Additional advantages and modification will readily occur to those skilled in art. Therefore, the invention in its broader aspect is not limited to specific details and representative embodiments shown and described herein. Accordingly various modifications may be made without departing from the spirit or scope of the general invention concept as defined by the appended claims and their equivalents.