A Scalable and Privacy-Preserving Machine Learning Framework for Early Detection of Chronic Diseases

Abstract

The rising cases of chronic illnesses like Chronic Kidney Disease and Heart Failure has posed a major burden on healthcare systems worldwide especially with late diagnosis and the lack of access to early intervention. This research suggests a scalable and privacy-conserving machine learning system that can be used to promote the early detection of chronic diseases without having access to sensitive information about patients.

This framework integrates Differential Privacy through the Laplacian mechanism to provide privacy of the data and at the same time preserve the utility of the data in analysis. De-identified datasets are made publicly available to remove the reliance on raw patient records. Random Forest and Gradient Boosting classifiers are lightweight machine learning models that are used because of their robustness, high predictive capability, and ability to be deployed in resource-constrained environments. One area of interest in this study is the privacy utility trade-off evaluation through the analysis of how varying privacy budget (ε) values affect the model accuracy. The experimental outcomes indicate that the proposed system attains high classification accuracy with an accuracy of 85% to 92% accuracy given moderate privacy conditions. The results validate the claim that ensemble learning models can work well in the context of noise added by privacy mechanisms without losing meaning data patterns.

It can also be supplemented by edge computing to provide real-time data processing/prediction of wearable device or IoT-based healthcare monitoring system to minimize the latency and the need to rely on a centralized infrastructure. This improves scalability and applicability of the framework in remote regions and underserved regions.

Overall, the proposed framework is a viable and effective solution to construct safe, scalable, and intelligent healthcare systems. It demonstrates that privacy-preserving techniques may be successfully implemented with the use of machine learning to promote early disease identification and comply with the data protection laws.

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