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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| Published in: | Journal of healthcare engineering Vol. 2022; pp. 1 - 12 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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12.04.2022
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| ISSN: | 2040-2295, 2040-2309, 2040-2309 |
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| Abstract | 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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| AbstractList | 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. 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.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. |
| Author | Ansarullah, Syed Immamul Kumhar, Sajadul Hassan Kumar, Dr. Pradeep Mohsin Saif, Syed Abdul Basit Andrabi, Syed Kirmani, Mudasir M. |
| AuthorAffiliation | 3 Research Scholar at the Department of Computer Science, Hyderabad, India 1 Lecturer at the Department of Computer Science, Govt. Degree College Sumbal, J&K, India 6 Professor at the Department of Computer Science and Information Technology, MANUU, Hyderabad, India 2 Research Coordinator at KWINTECH-R LABS (V), Kwintech-Rlabs(V), J&K, India 4 Research Scholar at the Department of Computer Science, Sehore, India 5 Assistant Professor at the Department of Computer Science, Division of Social Science, FoFy, SKAUST-Kashmir, Srinagar, India |
| AuthorAffiliation_xml | – name: 2 Research Coordinator at KWINTECH-R LABS (V), Kwintech-Rlabs(V), J&K, India – name: 5 Assistant Professor at the Department of Computer Science, Division of Social Science, FoFy, SKAUST-Kashmir, Srinagar, India – name: 6 Professor at the Department of Computer Science and Information Technology, MANUU, Hyderabad, India – name: 3 Research Scholar at the Department of Computer Science, Hyderabad, India – name: 4 Research Scholar at the Department of Computer Science, Sehore, India – name: 1 Lecturer at the Department of Computer Science, Govt. Degree College Sumbal, J&K, India |
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| Cites_doi | 10.1109/jec-ecc.2012.6186978 10.11989/JEST.1674-862X.80904120 10.1161/cir.0000000000000757 10.1109/aiccsa.2008.4493524 10.1109/ACCESS.2020.3006424 10.1016/j.cmpb.2013.03.004 10.1586/14737159.2015.1109450 10.1109/compe49325.2020.9200024 10.5120/ijca2016911187 10.1109/BRACIS.2016.018 10.1016/j.cpcardiol.2009.10.002 10.3390/electronics8070768 10.3390/informatics8040079 10.1371/journal.pmed.1002513 10.1109/ACCESS.2020.2980739 10.1093/ije/dyab029 10.1109/icaecc.2014.7002426 10.1016/j.cmpb.2017.01.004 10.1109/ACCESS.2021.3123456 10.1001/jamanetworkopen.2020.4669 10.1161/HHF.0b013e318291329a 10.1155/2022/9580896 10.1016/j.cmpb.2007.07.013 10.1109/ACCESS.2020.2974687 10.1371/journal.pone.0235758 10.1007/978-3-030-05318-5_1 10.1109/bmei.2009.5301650 10.1007/s13369-013-0934-1 10.1007/s13675-019-00115-7 |
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| SubjectTerms | Cluster Analysis Heart Diseases Humans Machine Learning Risk Assessment Support Vector Machine |
| Title | An Intelligent and Reliable Hyperparameter Optimization Machine Learning Model for Early Heart Disease Assessment Using Imperative Risk Attributes |
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