Ensemble Model Based Disease Analysis in Healthcare System

Due to rising rates of illness and death brought on by changes in the environment and food supply, healthcare disease is a serious threat to human existence. Accurate diagnosis and prediction are essential for many healthcare alerts, including prevention, early detection, and treatment. With the use of various technology tools, medical personnel need more accurately monitor, predict, and diagnose disease problems. However, the majority of prediction models just identify if people are sick and seldom go on to assess how serious the sickness is happened. In this work, we suggested a machine learning-based model for cardiac issue prediction that can simultaneously manage complicated and binary classifications. Machine learning techniques like Random Forest, K-Nearest Neighbours (KNN), and logistic regression are often used in data processing, and they have all shown promise in the prediction of cardiac disease. Additionally, we used the aforementioned techniques to create the ensemble model, illustrate the associated experiments, and contrast them with conventional techniques. According to assessment measures, the ensemble model performs exceptionally well. According to our experiment, SVM + RF performed better than other approaches and achieved 98% accuracy.