Ensemble Model for Exploratory Data Analysis and Prediction of Cardiomyopathy

This study intends to develop a dependable forecasting methodology for detecting cardiomyopathy via ML (machine learning) techniques such as Random Forests (RF), Decision Trees (DT), and K-Nearest Neighbours (KNN), hence improving prediction accuracy through the integration of different algorithms. EDA (Exploratory data analysis), which includes pre-processing procedures such as feature normalization and encoding categorical variables, helps in understanding the features of the dataset. We are using random data samples to train each classifier, and we'll evaluate their performance using metrics like confusion matrices and accuracy. A voting classifier is used to create the ensemble model, which combines predictions from different classifiers to maximize their combined intelligence and improve overall predictive accuracy. Finally, the ensemble model will predict cardiomyopathy in newly collected patient data, enabling early detection and personalized treatment planning. By incorporating advanced machine learning techniques, this study improves the way doctors make decisions and diagnose heart problems, leading to better treatment outcomes in cardiovascular medicine.