Comprehensive Machine Learning Framework for Heart Disease, Hypertension, and Diabetes Assessment

There are more illnesses every day, and detection takes too long. Nowadays, unhealthy lifestyles, junk food, and poor habits lead to many issues and potentially fatal illnesses. Following the COVID-19 pandemic in India, illness outbreaks such as heart disease, diabetes, and hypertension have impacted individuals globally. Currently, these illnesses are the leading cause of mortality for all ages. The health industry needs to improve illness prediction using Machine Learning (ML) approaches. Machine Learning (ML) within Artificial intellect may mimic human intellect and handle enormous amounts of data. Machine Learning single classifiers are insufficient for accurate and efficient classification nowadays. Hybrid Machine Learning Models are used to combine several classifiers. Prior study compared classifier ensembles for disease prediction. No universal model was created for varied illnesses. In the Covid-19 epidemic, people with hypertension, heart, or diabetes had a poor survival rate and were the main cause of mortality. Classifiers may classify or forecast illnesses. By using historical data, we were able to make predictions for cardiovascular condition, diabetes, and high blood pressure by using the Hybrid Ensemble Common Model. Using LightGBM, Random Forest, and KNN as Ensemble Classifiers, the Voting classifier is utilised for final output. The final output is captured after cross-validation.