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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.

Computational Intelligence and Neuroscience

|March 3, 2022

Summary

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.

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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.