Abstract
This study evaluates and compares the performance of different machine learning techniques on predicting the individuals at risk of developing hypertension, and who are likely to benefit most from interventions, using the cardiorespiratory fitness data. The dataset of this study contains information of 23,095 patients who underwent clinician- referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 10-year follow-up. The variables of the dataset include information on vital signs, diagnosis and clinical laboratory measurements. Six machine learning techniques were investigated: LogitBoost (LB), Bayesian Network classifier (BN), Locally Weighted Naive Bayes (LWB), Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Tree Forest (RTF). Using different validation methods, the RTF model has shown the best performance (AUC = 0.93) and outperformed all other machine learning techniques examined in this study. The results have also shown that it is critical to carefully explore and evaluate the performance of the machine learning models using various model evaluation methods as the prediction accuracy can significantly differ.
Highlights
Hypertension is a major condition that can lead to many severe illnesses such as stroke and heart disease [1]
We investigate and compare the performance of several machine learning techniques that use cardiorespiratory fitness data for predicting individuals at risk of developing hypertension who are most likely to benefit from interventions
Using the data of this study, we evaluated and compared six different classification techniques for predicting the Hypertension outcome: Artificial Neural Network (ANN), LogitBoost (LB), Locally Weighted Naive Bayes (LWB), Random Tree Forest (RTF), Sup- port Vector Machine (SVM) and Bayesian Network (BN)
Summary
Hypertension is a major condition that can lead to many severe illnesses such as stroke and heart disease [1]. Risk assessment of the disease is significantly complicated and depends on many factors and environmental conditions that can significantly raise blood pressure readings. According to the World Health Organization (WHO), high blood pressure causes one in every eight deaths and Hypertension is considered the third leading killer in the world [2]. There are around a billion of hypertensive patients around the word and around four million patients die every year. In the Middle Eastern region, cardiovascular disease and stroke are the main cause of death and illness. They resulted in 31% of deaths and currently
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