Predicting survival of patients with heart failure by optimizing the hyperparameters

Abstract

Heart failure is a prominent global cause of mortality. Heart failure is a medical condition characterized by the heart's inability to adequately circulate blood throughout the body or meet its needs. The rising expenses associated with conventional medical treatments for heart failure diagnosis have underscored the significance of developing diagnostic systems that utilize machine learning approaches. We employed various machine learning techniques on a heart failure dataset from the literature, specifically focusing on the survival status of heart failure patients. After employing the hold-out validation technique to prevent overfitting, the survival status of patients was evaluated by utilizing GridSearchCV hyperparameter optimization on classifiers such as Naive Bayes, Support Vector Machines, Decision Trees, Random Forests, K-Nearest Neighbor, Discriminant Analysis, and Extreme Gradient Boosting. Our performance measurement results show that the Decision Trees, Support Vector Machines, and Naive Bayes algorithms are prominent algorithms for the relevant data. While Decision Tree has the highest accuracy value, Support Vector Machines gives the lowest false negative rate, a crucial metric in medical decisions. Additionally, Naive Bayes has very similar results to the Support Vector Machines algorithm. While the straightforward and effective Decision Tree model, which has minimal pre-processing and does not require input scaling, is an easy-to-use method, Support Vector Machines and Naive Bayes which has low false negative rate values may help decision-making by reducing the risk of misdiagnosis.