Diagnosify: Multidisease Forecast - An Integrated Machine Learning Approach for Disease Prediction and Early Intervention in Healthcare

Abstract

Joybir Singh*, Vinod Kumar and Nikhil Kumar Chahar Lalmannrt

Developments in artificial intelligence and machine learning have been a catalyst for paradigm change in healthcare sectors. Along with modernization of existing healthcare techniques, these technologies have allowed innovators to come out with path-breaking approaches regarding disease diagnosis and prevention, "early on." Of course, one such groundbreaking approach is "Diagnosify: Multidisease Forecast," a sophisticated website that has the "breathtaking" potentiality of forecasting the likeliness of three major diseases - Diabetes, Parkinson's Disease, and Heart Disease. This is through the aid of individual health data used by the platform, in turn giving people and the healthcare providers a powerful tool for proactive health management. The aim of this study is to present a thorough analysis of the Diagnosify system. In this, we analyze its fundamental architecture, the depth of its machine learning models, and the advanced approaches used in predictive analytics. It extensively researches every disease model-from diabetes and Parkinson's to heart disease-and indicates what is unique about them and their contributions to the prediction. The document then details how data is collected in an effort that supports the work of the Diagnosify platform. It shares a review process that has ensured the precision and dependability of its prediction models. This encompasses an analysis of parameters used in the evaluation of success diagnoses generated by Diagnosify.

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