Significance of Visible Non-Invasive Risk Attributes for the Initial Prediction of Heart Disease Using Different Machine Learning Techniques.

Autor: Ansarullah SI; Lecturer at the Department of Computer Science, Cluster University, Jammu, India., Saif SM; Assistant Professor at the Department of Information Technology, IUST Awantipora, Kashmir, India., Kumar P; Associate Professor at the Department of Computer Science and Information Technology, MANUU, Hyderabad, India., Kirmani MM; Assistant Professor at the Department of Fisheries, SKAUST, Kashmir, India.
Jazyk: angličtina
Zdroj: Computational intelligence and neuroscience [Comput Intell Neurosci] 2022 Feb 21; Vol. 2022, pp. 9580896. Date of Electronic Publication: 2022 Feb 21 (Print Publication: 2022).
DOI: 10.1155/2022/9580896
Abstrakt: 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.
Competing Interests: The authors declare that they have no conflicts of interest.
(Copyright © 2022 Syed Immamul Ansarullah et al.)
Databáze: MEDLINE
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