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Machine learning approach for predicting cardiovascular disease in Bangladesh: evidence from a cross-sectional study
Sorif Hossain1, Mohammad Kamrul Hasan2, Mohammad Omar Faruk3
1Department of Statistics, Noakhali Science and Technology University, Noakhali, 3814, Bangladesh. shossain9@isrt.ac.bd.
Insights
Cardiovascular disorders (CVDs) are a growing concern, especially in Bangladesh. The Random Forest model demonstrated superior accuracy (98.04%) and precision (96.15%) in predicting CVD risk, offering a promising tool for clinical practice.
Area of Science:
- Medical Informatics
- Machine Learning in Healthcare
- Public Health
Background:
- Cardiovascular disorders (CVDs) are the leading global cause of death.
- Bangladesh faces a rising burden of CVDs, surpassing infectious diseases.
- CVDs like heart failure and stroke significantly impact lower- and middle-income countries (LMICs).
Purpose of the Study:
- To identify critical factors influencing cardiovascular disease.
- To develop and evaluate machine learning models for predicting CVD risk.
- To enhance clinical decision-making for CVD prognosis.
Main Methods:
- A dataset of 391 CVD patients and 260 controls was analyzed.
- Statistical tests (crosstabs, chi-square) assessed variable associations.
- Classifiers including Logistic Regression, Naïve Bayes, Decision Tree, AdaBoost, Random Forest, and Ensemble methods were employed for CVD prediction.
- Performance was evaluated using accuracy, sensitivity, specificity, and AU-ROC.
Main Results:
- The Random Forest classifier achieved the highest accuracy (98.04%) and precision (96.15%).
- Random Forest demonstrated robust recall (100%) and a high F1 score (97.7%).
- The highest Area Under the Receiver Operator Characteristic (AU-ROC) curve value of 0.989 was obtained with Random Forest.
Conclusions:
- The Random Forest technique is highly recommended for developing CVD prediction systems.
- This predictive model can serve as a valuable tool for clinicians to assess patient CVD prognosis.
- Implementing this model has the potential to significantly impact clinical practice in managing cardiovascular diseases.
Background:
Cardiovascular disorders (CVDs) are the leading cause of death worldwide. Lower- and middle-income countries (LMICs), such as Bangladesh, are also affected by several types of CVDs, such as heart failure and stroke. The leading cause of death in Bangladesh has recently switched from severe infections and parasitic illnesses to CVDs.
Materials And Methods:
The study dataset comprised a random sample of 391 CVD patients' medical records collected between August 2022 and April 2023 using simple random sampling. Moreover, 260 data points were collected from individuals with no CVD problems for comparison purposes. Crosstabs and chi-square tests were used to determine the association between CVD and the explanatory variables. Logistic regression, Naïve Bayes classifier, Decision Tree, AdaBoost classifier, Random Forest, Bagging Tree, and Ensemble learning classifiers were used to predict CVD. The performance evaluations encompassed accuracy, sensitivity, specificity, and area under the receiver operator characteristic (AU-ROC) curve.
Results:
Random Forest had the highest precision among the five techniques considered. The precision rates for the mentioned classifiers are as follows: Logistic Regression (93.67%), Naïve Bayes (94.87%), Decision Tree (96.1%), AdaBoost (94.94%), Random Forest (96.15%), and Bagging Tree (94.87%). The Random Forest classifier maintains the highest balance between correct and incorrect predictions. With 98.04% accuracy, the Random Forest classifier achieved the best precision (96.15%), robust recall (100%), and high F1 score (97.7%). In contrast, the Logistic Regression model achieved the lowest accuracy of 95.42%. Remarkably, the Random Forest classifier achieved the highest AUC value (0.989).
Conclusion:
This research mainly focused on identifying factors that are critical in impacting patients with CVD and predicting CVD risk. It is strongly advised that the Random Forest technique be implemented in a system for predicting cardiac diseases. This research may change clinical practice by providing doctors with a new instrument to determine a patient's CVD prognosis.
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