MINING CLINICAL DATA FOR HYPERTENTION PREDICTION PERFORMANCE EVALUATION OF SIX SUPERVIVED LEARNING ALGORITHMS
DOI:
https://doi.org/10.33003/fjs-2026-1001-4543Keywords:
Hypertension, Imbalance data, Machine Learning, Prediction model, Supervised learningAbstract
Machine learning classifiers usage in medicine has been on a rise over the years due to increase in available clinical data. Hypertension, one of the cardiovascular heart diseases is a global health concern with several risk factors where early detection could prevent related heart diseases complications. With abundant clinical data piling within our health facilities here in Nigeria, little or non-research on hypertension prediction using local dataset has been done. This research work is aimed at developing models for predicting individuals with likelihood of hypertension even without necessary going through medical procedures. A sample of 294 clinical data from Specialist Hospital Gashua was used. Random Forest, Decision tree, Artificial neural network, Support vector machine, Logistic regression and Naïve bayes algorithms were used for classification. To minimised issues of over-fitting due to the number of available data, ADASYN balancing technique was used for data balancing. Using Root Means Square Error (RMSE), Accuracy, Recall, F1-score and ROC values as parameter matrix for evaluating the models, our results showed that machine learning can be utilised in predicting hypertension diseases for quick preventive measures. Furthermore, this research work further illustrate that Random Forest has proven to be the most efficient and best algorithms for hypertension prediction having performance supremacy over all others five algorithms with 92% prediction accuracy, 100% Precision, Recall values of 91%, 91% F1-score value, RMSE of 29% and AUC value of 97%. Thereby, stressing the significant of balancing dataset for enhanced model accuracy (and all other metrics).
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Copyright (c) 2026 Awuza Abdulrashid Egwa, Abdullahi Musa Bello, Sikirat Kehinde Aina, Saadatu Gimba
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