Forecasting and Predictive Model Building for Heart Failure Data using Machine Learning Techniques

Heart failure is a critical medical condition with significant global morbidity and mortality rates. Early prediction of Heart Failure risk is paramount for timely intervention and improving patient outcomes. Leveraging the power of machine learning, this study conducts an in-depth examination of Heart Failure prediction using a comprehensive dataset. The dataset encompasses clinical, demographic and laboratory data of Heart Failure patients. A variety of machine learning techniques like logistic regression, decision tree and random forest are harnessed to construct predictive models for Heart Failure. The analysis integrates essential data preprocessing techniques like missing value imputation, feature scaling and feature selection to enhance model robustness. Metrics over evaluation consist of precision, recall, accuracy, F1-score and area under receiver operating characteristic curve employed to gauge model efficiency. This study comparing the performance of Logistic Regression, Random Forest and Decision Tree to predict heart failure data. The results are provided that the Random Forest algorithm is the finest performance with accuracy 0.99, precision 0.99, recall and F1 score 0.99 in detecting heart failure than Logistic Regression and Decision Tree. This study highlights the potential of machine learning, especially Random Forest, for early heart failure risk assessment, enabling timely interventions, reducing healthcare costs, and supporting personalized patient care. Exploring advanced machine learning techniques and real-time predictive models could further enhance accuracy and clinical applicability.