Prediction of Heart Disease Risk among Patients in Federal Medical Centre, Abeokuta Using Naïve Bayes
Olawale Basheer Akanbi
*
Department of Statistics, University of Ibadan, Ibadan, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
Heart disease is a silent killer, which can cause sudden death of individuals without obvious symptoms. The risk of heart disease is a sign in human beings that must not be neglected. Therefore, this study aimed to predict heart disease among patients in the Federal Medical Hospital Centre (FMC), Abeokuta. Descriptive statistics and data visualization techniques were used to gain insights into the distribution and relationships among the variables. Subsequently, a Naive Bayes classifier model was built using 80% of the data for training and 20% for testing. In addition, a Decision Tree Algorithm (DT) model was used to compare the performance of the Naive Bayes model. The performances of the two models were evaluated using accuracy, sensitivity, ROC-AUC, specificity, precision, and the F1-score. The Naive Bayes model achieved an overall accuracy of 83.61%, precision of 89.29%, recall of 78.12%, F1-Score of 83.33%, ROC-AUC of 90%, sensitivity of 78.12%, and specificity of 89.66. On the other hand, they were compared with the Decision Tree (DT) model which achieved an overall accuracy of 75.41%, precision of 77.42%, recall of 75%, F1-Score of 76.19%, ROC-AUC of 84.54%, sensitivity of 75%, and specificity of 75.86%. Similarly, the confusion matrix for both analyses gave the correct classification of 25 and 22 cases of patients who have heart disease while their wrong classification was 7 and 10 cases of patients who have no heart disease respectively. Furthermore, the importance of features carried out on both showed that the most significant features are the maximum heart rate achieved, fasting blood sugar, resting blood pressure, and chest pain respectively for Naive Bayes and Decision Tree. The findings of the analysis showed that the Naive Bayes model outperformed the Decision Tree in every aspect of the analyses in predicting the risk of heart disease based on the data used and, it suggested that medical health insurance should consider incorporating predictive modelling techniques like Naive Bayes into their risk assessment algorithms, which can be of great use in the medical line.
Keywords: Naïve bayes classifier, decision tree algorithm, confusion matrix, feature importance, maximum heart rate