B.Tech (Scholar),School of Computer Science and Engineering,Sandip University,Nashik,India
Associate Professor, School of Computer Science and Engineering,Sandip University,Nashik,India
Assistant Professor, School of Computer Science and Engineering,Sandip University,Nashik,India
Lifestyle diseases like diabetes, heart disease, and obesity are a world health concern because their rates are going up and their effects are getting worse. Predicting and diagnosing these diseases early is important if you want to stop them from getting worse, lower healthcare costs, and improve patient outcomes. Modern machine learning methods are one of the best ways to find lifestyle diseases early and figure out how likely they are to happen. The summary of this study talks about how advanced machine learning models can be used to find lifestyle-related diseases early. We look into how different machine learning methods, such as decision trees, support vector machines, random forests, and deep learning models, can be used to analyse data from a wide range of sources, such as EHRs, wearables, and information about people's daily routines. Using cutting-edge data analytics methods, the sophisticated machine learning models can find trends and correlations in the data that haven't been seen before. This makes it possible to find diseases that could be lifethreatening early on. These models take into account a wide range of factors, such as age, gender, health state, family history, and dietary and lifestyle preferences. Using this information, the models give each person a unique risk score, which lets doctors give more accurate diagnoses and tips on how to stay healthy.