Machine learning approach for predicting heart and diabetes diseases using data-driven analysis

Abstract

Environmental changes and food habits affect people's health with numerous
diseases in today's life. Machine learning is a technique that plays a vital role
in predicting diseases from collected data. The health sector has plenty of
electronic medical data, which helps this technique to diagnose various
diseases quickly and accurately. There has been an improvement in accuracy
in medical data analysis as data continues to grow in the medical field. Doctors
may have a hard time predicting symptoms accurately. This proposed work
utilized Kaggle data to predict and diagnose heart and diabetic diseases. The
diseases heart and diabetes are the foremost cause of higher death rates for
people. The dataset contains target features for the diagnosis of heart disease.
This work finds the target variable for diabetic disease by comparing the
patient's blood sugars to normal levels. Blood pressure, body mass index
(BMI), and other factors diagnose these diseases and disorders. This work
justifies the filter method and principal component analysis for selecting and
extracting the feature. The main aim of this work is to highlight the
implementation of three ensemble techniques-Adaptive boost, Extreme
Gradient boosting, and Gradient boosting-as well as the emphasis placed on
the accuracy of the results.