Hybrid Machine Learning Algorithm for Prediction of Various Diseases

Abstract

Many diseases are increasing day by day and it takes too much time to detect. With today s improper lifestyle, junk food, and bad habits we are facing so many problems in our lives and because of this, we are inviting unwanted lethal diseases. In India after the Covid-19 pandemic so many diseases have spread their era like, Heart Disease, Diabetes, Hypertension, Liver Diseases, Lung cancer, and Brain Stroke are six of them and it has affected several people worldwide. In recent times, These diseases have become the major reason for death among people of any age group. Therefore, the enhancement for predicting this kind of disease is required in the health sector with the help of different Machine Learning (ML) methods. Machine Learning (ML) is the subset of Artificial intelligence that can imitate human intelligence and it can process large information. Nowadays alone Machine Learning s single classifier is not enough to classify with higher accuracy and less time. So we can ensemble many classifiers to each other, this ensemble method is called Hybrid Machine Learning Model. In previous research studies, comparisons of various classifier ensembles are used for Disease prediction. But they didn t build any common model for various diseases. During the Covid-19 pandemic, the survival rate was low in hypertension, heart, or diabetes patients and the majority of death during the Covid-19 pandemic was due to these diseases. The classification or prediction of those diseases can be done by classifiers. In this study, we used Hybrid Ensemble Common Model (HECM) for predicting Heart, Diabetes, Hypertension Disease Liver diseases, Lung Cancer, and Brain Stroke possibilities based on the collection of historical datasets. LightGBM, Random Forest, and KNN are used as Ensemble Classifiers then output is given to the Voting classifier for final output. Cross Validation is done at last and the final output is recorded.