Analysis and Visualize the Predictive Model Performance: Manual Vs Automated Machine Learning (AutoML) Algorithms for Heart Failure Prediction

Heart failure (HF) is a common complication of cardiovascular diseases. This research focuses on assessing the effectiveness of different models for predicting HF using both Traditional Machine Learning (TML) methods and Automated Machine Learning (AutoML) approaches. TML models need extensive manual tuning and expert knowledge for algorithm selection and optimization, making the process slow and susceptible to human error. To tackle this challenge, the work proposed an AutoML approach utilizing the AutoGluon framework for predicting HF. The main goal of this study is to automate the process of selecting the most efficient model. This study compares a total of twenty (20) individual-trained ML models, consisting of fourteen (14) from AutoML and six (6) from TML. In TML, Logistic Regression (LR) produced the highest 87.50% accuracy and ROC-AUC of 88.83% compared to Support Vector Models (SVM), Decision Trees (DT), Gaussian Naïve Bayes (GNB), Random Forests (RF) and K-Nearest Neighbors (KNN). In AutoML, the CatBoost model outperforms the other thirteen algorithms with the highest accuracy of 99.39% and ROC-AUC of 99.89%. The results show that an AutoML based algorithm called the CatBoost model gives the most accurate model among all 20 models. SHAP was employed to interpret the top-performing model, increasing its transparency and usability.