Methodology for Ensuring Secure Disease Prediction using Machine Learning Techniques

Abstract

In today's digital world, the e-healthcare system has increased the patient data in vast amount. Protecting this confidential patient data from unauthorized access and tampering is crucial as the data contains sensitive details regarding patient’s health and any tampering on such details would result in manipulation of patient data which could lead to misdiagnosis and incorrect treatment plans. Conventional healthcare systems lack the ability to secure patient data from unauthorized access which eventually leads to data tampering and data loss. Data security and data privacy are crucial components within the healthcare sector and can be enhanced by the utilization of blockchain framework. Within the healthcare domain, disease identification and prediction is also a critical challenge. This study focuses on disease detection and prediction such as diabetes mellitus and blood pressure by implementing ML models such as Decision Tree, SVM, KNN, Naive Bayes, Random Forest and ensemble learning while maintaining the integrity of patient sensitive health data. The diagnostic results predicted by classifier and new patient data have been stored on smart contracts. Only authorized persons such as healthcare professionals can have access to patient sensitive health related data and diagnostic results predicted by the classifier. The research aims to enhance the efficiency of machine learning classifiers along with data integrity.