<![CDATA[Environmental changes and food habits affect people's health with numerousdiseases in today's life. Machine learning is a technique that plays a vital rolein predicting diseases from collected data. The health sector has plenty ofelectronic medical data, which helps this technique to diagnose variousdiseases quickly and accurately. There has been an improvement in accuracyin medical data analysis as data continues to grow in the medical field. Doctorsmay have a hard time predicting symptoms accurately. This proposed workutilized Kaggle data to predict and diagnose heart and diabetic diseases. Thediseases heart and diabetes are the foremost cause of higher death rates forpeople. The dataset contains target features for the diagnosis of heart disease.This work finds the target variable for diabetic disease by comparing thepatient's blood sugars to normal levels. Blood pressure, body mass index(BMI), and other factors diagnose these diseases and disorders. This workjustifies the filter method and principal component analysis for selecting andextracting the feature. The main aim of this work is to highlight theimplementation of three ensemble techniques-Adaptive boost, ExtremeGradient boosting, and Gradient boosting-as well as the emphasis placed onthe accuracy of the results.]]>
Copyrights © 2023
