Heart disease prediction using distinct artificial intelligence techniques: performance analysis and comparison

Hossain, I.; Maruf, M.; Khan, Md. Ashikur Rahman; Prity, Farida Siddiqi; Fatema, Sharmin; Ejaz, Md. Sabbir; Khan, M.A.

doi:10.1007/s42044-023-00148-7

Cited by 22 publications

(16 citation statements)

References 60 publications

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“…Cardiovascular diseases (CVD) rank highest among all causes of death globally [32]. The late detection of cardiac issues significantly reduces the prognosis for patients [33] the best prediction result with 97.7% accuracy which is similar to previous studies [11]. Similar to our study, the previously conducted study found that the Random Forest (RF) approach achieved almost 100% accuracy, sensitivity, and specificity in identifying features with the highest likelihood of heart disease [32].…”

Section: Discussionsupporting

confidence: 89%

Machine Learning Approach to Predict Cardiovascular Disease in Bangladesh: Evidence from a Cross-Sectional Study in 2023.

Hossain,

Hasan,

Faruk

et al. 2023

Preprint

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Background Cardiovascular disorders (CVDs) are widely considered the leading cause of death worldwide. Lower and middle-income countries (LMICs) like Bangladesh are also affected by several types of CVDs such as heart failure and stroke. The leading factors of death in Bangladesh have increasingly switched from severe infections and parasitic illness to CVDs recently. Materials and methods The study dataset is a random sample of the 391 CVD patients' medical records collected between August 2022 and April 2023 using simple random sampling. Moreover, 260 data are also collected from individuals with no CVD problem for comparison purposes. Crosstabs and chi-square are used to find the association between CVD and explanatory variables. Logistic regression, Naïve Bayes classifier, Decision Tree, AdaBoost classifier, Random Forest, Bagging Tree, and Ensemble learning classifiers are used to predict CVD in this study. The performance evaluations encompassed accuracy, sensitivity, specificity, and the area under the receiver operator characteristic (AU-ROC) curve. Result Random Forest has 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 achieves the best precision (96.15%), robust recall (100%), and a high F1 score (97.7%). In contrast, the Logistic Regression model achieves the lowest accuracy at 95.42%. Remarkably, the Random Forest classifier attains the highest AUC value (0.989). Conclusion This research is mainly focused on identifying factors that are critical in impacting CVD patients and predicting CVD risk. It is strongly advised that the Random Forest technique be implemented in the system for predicting cardiac disease. This research may change clinical practice by giving doctors a new instrument to determine a patient's prognosis for CVD.

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Section: Discussionsupporting

confidence: 89%

Machine Learning Approach to Predict Cardiovascular Disease in Bangladesh: Evidence from a Cross-Sectional Study in 2023.

Hossain,

Hasan,

Faruk

et al. 2023

Preprint

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“…In addition, the Random Forest classifier maintained a strong F1 score, strong recall, and high precision, achieving the highest accuracy of 98.04%. Random Forest produces the best prediction result with 97.7% accuracy, which is similar to previous studies [ 11 ]. Similar to our study, a previous study found that the Random Forest (RF) approach achieved almost 100% accuracy, sensitivity, and specificity in identifying features with the highest likelihood of heart disease [ 42 ].…”

Section: Discussionsupporting

confidence: 89%

“…The findings of previous studies clearly demonstrate that the Random Forest classifier outperforms all other classifiers in terms of expected accuracy and performance [ 11 ]. Incorporating the Random Forest approach into a system for CVD prediction is highly recommended.…”

Section: Discussionmentioning

confidence: 74%

“…Dyslipidemia, tobacco use, diabetes, hypertension, and overweight have also been reported as potential risk factors for heart failure in previous studies [ 9 , 10 ]. Another study conducted by Hossain et al (2023) found that age, sex, smoking, obesity, diet, physical activity, stress, chest pain type, previous chest pain, diastolic blood pressure, diabetes, and troponin were the most important factors for identifying CVD risk [ 11 ]. Different experiences at different stages of epidemiological transition and urbanization, with varying life expectancies, diverse demographic profiles, and differences in environmental and genetic risk factors, could explain the different relationships between these risk factors and CVD mortality in Asian and Western societies [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, machine learning models have been widely used to precisely predict CVD risk factors. Hossain et al (2023) analyzed a study 2023 to predicting the risk of heart failure using distinct artificial intelligence techniques (logistic regression, Naïve Bayes, K-nearest neighbor (K-NN), support vector machine (SVM), decision tree, random forest, and multilayer perceptron (MLP) [ 11 ]. In this study, the authors found that the Random Forest model achieved the highest accuracy rate (90%) compared to other machine learning models.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning approach for predicting cardiovascular disease in Bangladesh: evidence from a cross-sectional study in 2023

Hossain,

Hasan,

Faruk

et al. 2024

BMC Cardiovasc Disord

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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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Enhancing healthcare in the digital era: A secure e-health system for heart disease prediction and cloud security

vellore pichandi,

Janarthanan,

Annamalai

et al. 2024

Expert Systems with Applications

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Heart disease prediction using distinct artificial intelligence techniques: performance analysis and comparison

Cited by 22 publications

References 60 publications

Machine Learning Approach to Predict Cardiovascular Disease in Bangladesh: Evidence from a Cross-Sectional Study in 2023.

Machine Learning Approach to Predict Cardiovascular Disease in Bangladesh: Evidence from a Cross-Sectional Study in 2023.

Machine learning approach for predicting cardiovascular disease in Bangladesh: evidence from a cross-sectional study in 2023

Enhancing healthcare in the digital era: A secure e-health system for heart disease prediction and cloud security

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