A Novel Study on Machine Learning Algorithm-Based Cardiovascular Disease Prediction.
Cardiovascular disease (CVD) is a life-threatening disease rising considerably in the world. Early detection and prediction of CVD as well as other heart diseases might protect many lives. This requires tact clinical data analysis. The potential of predictive machine learning algorithms to develop t...
| Publicado en: | Health & Social Care in the Community pp. 1 - 11 |
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| Autores principales: | , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
2/20/2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=161985632&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 161985632 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09660410 EVX jtl: Health & Social Care in the Community issn: 09660410 maglogo: Y pubinfo: dt: 2/20/2023 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 161985632 161985632 161985632 10.1155/2023/1406060 161985632 ppf: 1 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Novel Study on Machine Learning Algorithm-Based Cardiovascular Disease Prediction. aug: au: Khan, Arsalan Qureshi, Moiz Daniyal, Muhammad Tawiah, Kassim affil: Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan sug: subj: Cardiovascular Diseases Prognosis Machine Learning Algorithms Decision Making Human Pakistan Academic Medical Centers Pakistan Exploratory Research Logistic Regression ROC Curve Descriptive Statistics Descriptive Research ab: Cardiovascular disease (CVD) is a life-threatening disease rising considerably in the world. Early detection and prediction of CVD as well as other heart diseases might protect many lives. This requires tact clinical data analysis. The potential of predictive machine learning algorithms to develop the doctor's perception is essential to all stakeholders in the health sector since it can augment the efforts of doctors to have a healthier climate for patient diagnosis and treatment. We used the machine learning (ML) algorithm to carry out a significant explanation for accurate prediction and decision making for CVD patients. Simple random sampling was used to select heart disease patients from the Khyber Teaching Hospital and Lady Reading Hospital, Pakistan. ML methods such as decision tree (DT), random forest (RF), logistic regression (LR), Naïve Bayes (NB), and support vector machine (SVM) were implemented for classification and prediction purposes for CVD patients in Pakistan. We performed exploratory analysis and experimental output analysis for all algorithms. We also estimated the confusion matrix and recursive operating characteristic curve for all algorithms. The performance of the proposed ML algorithm was estimated using numerous conditions to recognize the best suitable machine learning algorithm in the class of models. The RF algorithm had the highest accuracy of prediction, sensitivity, and recursive operative characteristic curve of 85.01%, 92.11%, and 87.73%, respectively, for CVD. It also had the least specificity and misclassification errors of 43.48% and 8.70%, respectively, for CVD. These results indicated that the RF algorithm is the most appropriate algorithm for CVD classification and prediction. Our proposed model can be implemented in all settings worldwide in the health sector for disease classification and prediction. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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