1. A secure and transparent blockchain-integrated machine learning framework for real-time predictive healthcare diagnostics, comprising a data acquisition layer for collecting patient data, a blockchain layer for immutable storage and secure sharing, a machine learning layer for predictive analysis, and an integration layer for providing diagnostic insights to healthcare professionals.

2. The framework as claimed in claim 1, wherein the blockchain layer employs cryptographic hashing and smart contracts to ensure tamper-proof storage, access control, and transparent data sharing among healthcare stakeholders including hospitals, laboratories, and insurance providers.

3. The framework as claimed in claim 1, wherein the machine learning layer utilizes supervised and unsupervised learning models trained on medical datasets to perform real-time predictive analytics, including anomaly detection, disease risk prediction, and early diagnostic alerts.

Description:Title:

A Secure and Transparent Blockchain-Integrated Machine Learning Framework for Real-Time Predictive Healthcare Diagnostics

Field of the Invention

[0001] Present invention includes Healthcare technology, specifically predictive diagnostics that use machine learning integrated with blockchain for secure, transparent, and real-time healthcare data management and diagnosis.

Background

[0002] Existing healthcare diagnostic systems often face challenges of data tampering, lack of trust, and insufficient mechanisms for maintaining the authenticity of medical records.
[0003] Sharing patient data across hospitals, labs, and insurance providers lacks transparency and secure auditing, leading to risks of unauthorized access and misuse.
[0004] While machine learning models improve predictive healthcare diagnostics, their reliability is questioned in the absence of secure and tamper-proof mechanisms for storing and processing data.
[0005] Blockchain technology offers immutability, decentralized security, and transparency. Integrating it with machine learning can overcome current limitations and ensure trustworthy, real-time predictive healthcare diagnostics.
[0006] All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.
[0007] In some embodiments, the numbers expressing quantities of ingredients, properties such as concentration, reaction conditions, and so forth, used to describe and claim certain embodiments of the invention are to be understood as being modified in some instances by the term “about.” Accordingly, in some embodiments, the numerical parameters set forth in the written description and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by a particular embodiment. In some embodiments, the numerical parameters should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of some embodiments of the invention are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values presented in some embodiments of the invention may contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements.
[0008] 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.
[0009] The recitation of ranges of values herein is merely intended to serve as a shorthand 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.
[0010] 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.

Objects of the Invention

[0011] To provide a secure and transparent framework for real-time predictive healthcare diagnostics by integrating blockchain technology with machine learning, ensuring data integrity, immutability, and trustworthy medical insights
[0012]. To enhance diagnostic accuracy and accountability by enabling tamper-proof storage, transparent sharing of healthcare data, and predictive analysis through advanced machine learning models governed by smart contracts.

 
Drawings

Figure 1

Brief Description of the Drawing

[0013] The figure 1 represents working model in the present invention with its prototype.
 
Detailed Description:

[0014] In figure 1, showing the input parameter; which is to be processed by the system 100.

[0015] Collect real-time patient data including vitals, medical history, sensor readings, and diagnostic reports using IoT-enabled devices, hospital databases, and electronic health records (EHR).
[0016] Clean, normalize, and preprocess the acquired data. Encrypt sensitive patient information before storing or transmitting it to ensure compliance with healthcare privacy regulations (HIPAA, GDPR).
[0017] Store patient records as immutable transactions on a blockchain. Use cryptographic hashing for data verification and smart contracts to manage permissions and access control among hospitals, laboratories, and insurance providers.
[0018] Train predictive models (e.g., logistic regression, random forest, deep learning) using historical medical datasets. Apply feature selection and dimensionality reduction techniques to optimize model performance.
[0019] Feed real-time patient data into the trained machine learning models. Generate predictions such as disease risk scores, anomaly detection, or early warning alerts.
[0020]. Record all diagnostic outputs on the blockchain to ensure transparency, immutability, and traceability. Use consensus mechanisms to validate and approve predictive results before dissemination.

[0021] Provide healthcare professionals with dashboards and mobile interfaces for visualizing predictions, diagnostic reports, and blockchain-based audit trails. Enable timely decision-making and personalized treatment recommendations.
[0022] In one aspect, various analytical, physical, and chemical methods may be employed to evaluate and enhance the performance of floral powders in latent fingerprint visualization. These methods can include characterization of particle size, morphology, and surface chemistry of floral powders using techniques such as scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), Fourier-transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD). Additionally, factors such as powder adhesion, contrast enhancement, background interference, and effectiveness across different substrate types (porous, non-porous, semi-porous) are considered to assess the reliability of floral powders. Comparative analysis with conventional fingerprint powders can further validate the efficiency, environmental safety, and sustainability of the proposed botanical formulations. Emphasis is placed on developing cost-effective, biodegradable, and non-toxic alternatives that meet forensic standards while minimizing ecological impact.
[0023] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[0024] 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.
, Claims:1. A secure and transparent blockchain-integrated machine learning framework for real-time predictive healthcare diagnostics, comprising a data acquisition layer for collecting patient data, a blockchain layer for immutable storage and secure sharing, a machine learning layer for predictive analysis, and an integration layer for providing diagnostic insights to healthcare professionals.
2. The framework as claimed in claim 1, wherein the blockchain layer employs cryptographic hashing and smart contracts to ensure tamper-proof storage, access control, and transparent data sharing among healthcare stakeholders including hospitals, laboratories, and insurance providers.
3. The framework as claimed in claim 1, wherein the machine learning layer utilizes supervised and unsupervised learning models trained on medical datasets to perform real-time predictive analytics, including anomaly detection, disease risk prediction, and early diagnostic alerts.