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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.

Diabetes mellitus (DM) is a serious worldwide health issue, and its prevalence is rapidly growing. It is a spectrum of metabolic illnesses defined by perpetually increased blood glucose levels. Undiagnosed diabetes can lead to a variety of problems, including retinopathy, nephropathy, neuropathy, and other vascular abnormalities. In this context, machine learning (ML) technologies may be particularly useful for early disease identification, diagnosis, and therapy monitoring. The core idea of this study is to identify the strong ML algorithm to predict it. For this several ML algorithms were chosen i.e., support vector machine (SVM), Naïve Bayes (NB), K nearest neighbor (KNN), random forest (RF), logistic regression (LR), and decision tree (DT), according to studied work. Two, Pima Indian diabetic (PID) and Germany diabetes datasets were used and the experiment was performed using Waikato environment for knowledge analysis (WEKA) 3.8.6 tool. This article discussed about performance matrices and error rates of classifiers for both datasets. The results showed that for PID database (PIDD), SVM works better with an accuracy of 74% whereas for Germany KNN and RF work better with 98.7% accuracy. This study can aid healthcare facilities and researchers in comprehending the value and application of ML algorithms in predicting diabetes at an early stage.

Diabetes mellitus (DM) is a serious worldwide health issue, and its prevalence is rapidly growing. It is a spectrum of metabolic illnesses defined by perpetually increased blood glucose levels. Undiagnosed diabetes can lead to a variety of problems, including retinopathy, nephropathy, neuropathy, and other vascular abnormalities. In this context, machine learning (ML) technologies may be particularly useful for early disease identification, diagnosis, and therapy monitoring. The core idea of this study is to identify the strong ML algorithm to predict it. For this several ML algorithms were chosen i.e., support vector machine (SVM), Naïve Bayes (NB), K nearest neighbor (KNN), random forest (RF), logistic regression (LR), and decision tree (DT), according to studied work. Two, Pima Indian diabetic (PID) and Germany diabetes datasets were used and the experiment was performed using Waikato environment for knowledge analysis (WEKA) 3.8.6 tool. This article discussed about performance matrices and error rates of classifiers for both datasets. The results showed that for PID database (PIDD), SVM works better with an accuracy of 74% whereas for Germany KNN and RF work better with 98.7% accuracy. This study can aid healthcare facilities and researchers in comprehending the value and application of ML algorithms in predicting diabetes at an early stage.