Application of Machine Learning Techniques for Predicting Hypertension Status and Indicators

Study’s Excerpt:

• This study compares five machine learning models to predict hypertension using patient data.


• Artificial neural networks achieved the highest AUC, making them the most effective classifier.


• Real data from Jigawa State, Nigeria, was used—an underexplored area in ML health analytics.


• Diabetes status and parental hypertension were top predictors, based on variable importance.


• Findings support ML as a powerful tool for hypertension prediction in resource-limited settings.

Full Abstract:

One significant reason of the suffering of productive age around the world is hypertension, a wonderful illness that intentionally aggravates the symptoms of renal, brain, heart, and other ailments.  Five machine learning approaches were used to classify the data according to training and testing sets: RF, CART, RT, SVM, and ANN.  A confusion matrix and a receiver operating characteristic curve were employed to evaluate the efficiency of the models.  This investigation assessed the effectiveness of five machine learning algorithms for forecasting hypertension and looked into the frequency of the condition.  The results showed that 60.42% of the studied population suffered from hypertension.  Furthermore, the comparison of machine learning models revealed that the artificial neural network outperformed the others, achieving AUC of 0.8694.  The variable importance ratings highlighted diabetes and parental hypertension, which were the most significant predictors of hypertension.  These findings can inform the development of effective predictive models and intervention strategies for hypertension management in Jigawa State of Nigeria.