Abstract: The leading cause of death nowadays is chronic disease. As a result, personal wellbeing has received a considerable boost as a healthcare preventative strategy. A notable development in data-driven healthcare technology is the creation of a prediction model for chronic diseases. In this situation, computational intelligence is used to analyze electronic health data to provide clinicians with knowledge that will help them make more informed decisions about prognoses or therapies. In this study, various classification algorithms have been implemented namely, Decision Tree, K-Nearest Neighbors, Logistic Regression, Multilayer Perceptron, Naïve Bayes, Random Forest, and Support Vector Machines to examine the medical records of patients in Kuwait who had chronic conditions. For predicting diabetes, the support vector machines classifier was the best classifier for predicting diabetes and kidney chronic diseases. For diabetes, it achieved 88.5% accuracy, and 93.6% f1-score, while for kidney; it achieved 94.9% and 92.6% accuracy and f1-score respectively. For predicting heart disease, MLP was the best and achieved 84.7%, and 87.8% accuracy and f1-score respectively.

Talal M. Alenezi, Taiseer H. Sulaiman and Amr M. AbdelAziz, “Applying Machine Learning Models to Electronic Health Records for Chronic Disease Diagnosis in Kuwait” International Journal of Advanced Computer Science and Applications(IJACSA), 14(11), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0141168

@article{Alenezi2023,
title = {Applying Machine Learning Models to Electronic Health Records for Chronic Disease Diagnosis in Kuwait},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0141168},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0141168},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {11},
author = {Talal M. Alenezi and Taiseer H. Sulaiman and Amr M. AbdelAziz}
}