A HEART DISEASE CLASSIFICATION MODEL ANALYSIS MADE BY DIFFERENT CLASSIFICATION ALGORITHMS AND DATASETS.
DOI:
https://doi.org/10.1808/eenfa293Keywords:
Datamining, Classification, Heart disease, random forests, K-Nearest neighbors (KNN), Support Vector Machines (SVM), Naïve Bayes and Logistic Regression, Cleveland Heart Disease datasetAbstract
Heart disease is a leading cause of death worldwide, necessitating the development of accurate and efficient diagnostic methods. Data mining techniques including classification have shown promise in analyzing large datasets and identifying patterns that can assist in the early detection of heart disease. In this report, a Heart diseases dataset is analyzed using different classification algorithms such as Random Forest, K-Nearest Neighbors, Support vector machines, Naïve Bayes and Logistic regression and more enhancement is performed where relevant. The dataset for experiment used here is Cleveland Heart Disease dataset available on UCI machine learning repository.
References
. Heart Disease UCI dataset: https://archive.ics.uci.edu/ml/datasets/heart+disease
. Kaggle Heart Disease dataset: https://www.kaggle.com/datasets/priyanka841/heart-disease-aaa-uci
. Cleveland Heart Disease dataset: https://archive.ics.uci.edu/ml/datasets/heart+disease
. Framingham Heart Study dataset: https://www.framinghamheartstudy.org/
. MIMIC-III dataset for cardiovascular diseases: https://mimic.physionet.org/
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