Significance of Visible Non-Invasive Risk Attributes for the Initial Prediction of Heart Disease Using Different
Syed Immamul Ansarullah1, Syed Mohsin Saif2, Pradeep Kumar3
1Lecturer at the Department of Computer Science, Cluster University, Jammu, India.
Insights
A new heart disease risk model uses non-invasive factors like age and smoking to predict risk accurately. The random forest model shows superior performance, offering a low-cost solution for early detection.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Heart disease is a leading global cause of death and a significant economic burden.
- There is a critical need for effective, affordable, and reliable heart disease risk assessment tools.
Purpose of the Study:
- To develop a heart disease risk evaluation model using significant non-invasive risk attributes.
- To investigate the reliability of attributes like age, blood pressure, BMI, smoking, and physical activity in heart disease prediction.
Main Methods:
- Feature selection techniques were employed to identify significant risk factors.
- Machine learning algorithms including random forest, Naïve Bayes, decision tree, support vector machine, and K nearest neighbor were tested.
- The model was developed using a Jupyter Notebook web application.
Main Results:
- The random forest model demonstrated superior predictive accuracy and a lower misclassification rate compared to other tested models.
- Performance was evaluated using measures such as error rate, AUROC, sensitivity, specificity, and accuracy.
Conclusions:
- The developed random forest heart disease risk model offers an effective and accurate prediction tool.
- This model is particularly valuable for regions lacking integrated primary medical care for early risk prediction.
Introduction:
Heart disease is emerging as the single most critical cause of death worldwide and is one of the costliest chronic conditions.
Purpose:
Stimulated by the increasing heart disease mortality rate incidents, an effective, low-cost, and reliable heart disease risk evaluation model is developed using significant non-invasive risk attributes. The significant non-invasive risk attributes like (age, systolic BP, diastolic BP, BMI, hereditary factor, smoking, alcohol, and physical inactivity) are identified by the help of medical domain experts, and their reliability in heart disease prediction is investigated through different feature selection techniques. Methodology. The enhancements of applying specific investigated techniques like random forest, Naïve Bayes, decision tree, support vector machine, and K nearest neighbor to the risk factors are tested. The heart disease risk assessment model is developed using the Jupyter Notebook web application, and its performance is tested not only through medical domain measures but also through the model performance measures. Findings. To evaluate heart disease risk evaluation model, we calculated measures of discrimination like error rate, AUROC, sensitivity, specificity, accuracy, precision, and so on. Experimental results show that the random forest heart disease risk evaluation model outperforms other existing risk models with admirable predictive accuracy and minimum misclassification rate. Originality. The heart disease risk evaluation model is developed based on novel non-invasive heart disease dataset, which consists of 5776 records. This dataset is collected from different heterogeneous data sources of Kashmir (India) through quantitative data collection methods. Research Implications. The risk model is applicable where people lack the facilities of integrated primary medical care technologies for untimely heart disease risk prediction. Future Work. To investigate deep learning and study the significance of other controlled attributes on different age and sex groups in the risk estimation of heart disease.
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