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The most common reason behind death all over the world is heart diseases. These conditions are to hit hardest in low- and middle-income nations, where 80% of premature heart attacks could be prevented. In this regard, early diagnosis also plays an important role in increasing patient health and survival rate from heart disease. The purpose of this study was to improve the forecasting power by means of feature selection techniques and then apply K-Fold cross validation in combination with high-performance ensemble machine learning (ML) methods (J48, Artificial Neural Networks (ANNs), Logistic Regression, Naive Bayes, K-Nearest Neighbors) by utilizing a dataset of 401,958 patients. Our experimental results demonstrate that ANNs achieve the highest accuracy at 91.48%. They also record the lowest Mean Absolute Error (MAE) of 0.13, highlighting their precision in predictions. Additionally, ANNs exhibit a low root Mean Squared Error (RMSE) of 0.26, further indicating their reliability in modeling. [ABSTRACT FROM AUTHOR]

In the 21st century, University educations are becoming a key pillar of social and economic life. It plays a major role not only in the educational process but also in the ensuring of two important things which are a prosperous career and financial security. However, predicting university admission can be especially difficult because the students are not aware of admission requirements. For that reason, the main purpose of this research work is to provide a recommender system for early predicting university admission. Therefore, the contributions are threefold: The first is to apply several Supervised Machine Learning algorithms namely Linear Regression, Support Vector Regression, Decision Tree Regression, and Random Forest Regression. The second purpose is to compare and evaluate algorithms used to create a predictive model based on various evaluation metrics. The last purpose is to determine the most important parameters that influence the chance of admission. The experimental results showed that the Random Forest Regression is the most suitable Machine Learning algorithm for predicting university admission. Also, the Cumulative Grade Point Average is the most important parameter that influences the chance of admission. [ABSTRACT FROM AUTHOR]

In recent years, the world's population is increasingly demanding to predict the future with certainty, predicting the right information in any area is becoming a necessity. One of the ways to predict the future with certainty is to determine the possible future. In this sense, machine learning is a way to analyze huge datasets to make strong predictions or decisions. The main objective of this research work is to build a predictive model for evaluating students' performance. Hence, the contributions are threefold. The first is to apply several supervised machine learning algorithms (i.e. ANCOVA, Logistic Regression, Support Vector Regression, Log-linear Regression, Decision Tree Regression, Random Forest Regression, and Partial Least Squares Regression) on our education dataset. The second purpose is to compare and evaluate algorithms used to create a predictive model based on various evaluation metrics. The last purpose is to determine the most important factors that influence the success or failure of the students. The experimental results showed that the Log-linear Regression provides a better prediction as well as the behavioral factors that influence students' performance. [ABSTRACT FROM AUTHOR]