aspg-3-2037-1791419455 20261008003055 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2023 13 1 Ahmed A. H. Alkurdi Department of Information Technology Management, Technical College of Administration, Duhok Polytechnic University, Duhok, KRG-Iraq; Department of Computer Science, College of Science, Nawroz Heart diseases are the primary cause of death worldwide. The approximate mortality rate due to cardiovascular diseases is a staggering 18 million lives per year. many human lives could be saved with early and accurate diagnosis and prediction of such conditions. Thus, the automation of such a process is crucial and achievable with the rise of machine learning and deep learning capabilities. However, patient data is riddled with issues which must be resolved before they can be used for heart disease prediction. This research aims to improve the accuracy of heart disease diagnosis by utilizing data preprocessing techniques and classification algorithms. These techniques may provide an insight into predicting cardiovascular diseases from subtle clues before any major symptoms arise. The study employs the Heart Disease UCI dataset and follows a systematic approach to train machine learning models in the process of heart disease diagnosis. The approach utilizes a variety of data preprocessing techniques to prepare the data for model training such as MEAN missing value imputation, Normalization, Synthetic Minority Over-sampling Technique (SMOTE), and Correlation. Afterward, the preprocessed data is fed into four popular classification algorithms: Decision Tree, Random Forest, Support Vector Machine (SVM), and k-Nearest Neighbors (k-NN). These algorithms provide a broad evaluation of the dataset. The proposed methodology demonstrates promising results which clearly highlight the value and significance of data preprocessing. This is evident from the achieved accuracy, precision, recall, F1 score and ROC AUC results. In summary, the importance of preprocessing and feature selection is distinct when dealing with datasets containing various challenges. These crucial processes play a central role in building a trustworthy and precise model for heart disease prediction. 2023 08 18 2037 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.130101 https://www.americaspg.com/journal/3/article/2037 https://www.americaspg.com/storage/51739481491.pdf