An Intelligent and Reliable Hyperparameter Optimization Machine Learning Model for Early Heart Disease Assessment Using Imperative Risk Attributes

Heart disease is a severe disorder, which inflicts an adverse burden on all societies and leads to prolonged suffering and disability. We developed a risk evaluation model based on visible low-cost significant noninvasive attributes using hyperparameter optimization of machine learning techniques. T...

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Vydáno v:Journal of healthcare engineering Ročník 2022; s. 1 - 12
Hlavní autoři: Ansarullah, Syed Immamul, Mohsin Saif, Syed, Abdul Basit Andrabi, Syed, Kumhar, Sajadul Hassan, Kirmani, Mudasir M., Kumar, Dr. Pradeep
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Hindawi 12.04.2022
Témata:
Cluster Analysis
Heart Diseases
Humans
Machine Learning
Risk Assessment
Support Vector Machine
ISSN:2040-2295, 2040-2309, 2040-2309
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Shrnutí:Heart disease is a severe disorder, which inflicts an adverse burden on all societies and leads to prolonged suffering and disability. We developed a risk evaluation model based on visible low-cost significant noninvasive attributes using hyperparameter optimization of machine learning techniques. The multiple set of risk attributes is selected and ranked by the recursive feature elimination technique. The assigned rank and value to each attribute are validated and approved by the choice of medical domain experts. The enhancements of applying specific optimized techniques like decision tree, k-nearest neighbor, random forest, and support vector machine to the risk attributes are tested. Experimental results show that the optimized random forest risk model outperforms other models with the highest sensitivity, specificity, precision, accuracy, AUROC score, and minimum misclassification rate. We simulate the results with the prevailing research; they show that it can do better than the existing risk assessment models with exceptional predictive accuracy. The model is applicable in rural areas where people lack an adequate supply of primary healthcare services and encounter barriers to benefit from integrated elementary healthcare advances for initial prediction. Although this research develops a low-cost risk evaluation model, additional research is needed to understand newly identified discoveries about the disease.
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Academic Editor: Suneet Kumar Gupta
ISSN:2040-2295
2040-2309
2040-2309
DOI:10.1155/2022/9882288