Chronic Disease Prediction using Machine Learning

Abstract: One of the biggest issues facing the healthcare industry is chronic diseases. The global populace consumes a lot of unhealthy food. Generally speaking, doctors must thoroughly review the patient's records in order to diagnose the ailment. Sometimes it is difficult for doctors to treat patients effectively since the diagnosis is manual. Chronic disease patients are becoming more and more numerous every day. Therefore, environmental threat assessment is important. Currently, the digitization of healthcare is taking advantage of advances in medical care in hospitals. The traditional style lacks the knowledge development to monitor and analyze problems and views of traditional situations and has been replaced by the process of gaining better understanding from clinical data through the use of predictive analytics and fact. time machine readable tools. Reduce the size to include conditions like heart, stroke, diabetes, and cancer across multiple datasets using keywords selected in the research using machine learning penalty debt. Feature selection plays an important role in machine learning by selecting key features for live event detection.

Keywords: MachineLearning; sklearn; Randomforest; Linear regression.