Mixed Machine Learning Approach for Efficient Prediction of Human Heart Disease by Identifying the Numerical and Categorical Features
Heart disease is a danger to people’s health because of its prevalence and high mortality risk. Predicting cardiac disease early using a few simple physical indications collected from a routine physical examination has become difficult. Clinically, it is critical and sensitive for the signs of heart...
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| Format: | Article |
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MDPI AG
2022-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/15/7449 |
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| author | Ghulab Nabi Ahmad Shafiullah Hira Fatima Mohamed Abbas Obaidur Rahman Imdadullah Mohammed S. Alqahtani |
| author_facet | Ghulab Nabi Ahmad Shafiullah Hira Fatima Mohamed Abbas Obaidur Rahman Imdadullah Mohammed S. Alqahtani |
| author_sort | Ghulab Nabi Ahmad |
| collection | DOAJ |
| description | Heart disease is a danger to people’s health because of its prevalence and high mortality risk. Predicting cardiac disease early using a few simple physical indications collected from a routine physical examination has become difficult. Clinically, it is critical and sensitive for the signs of heart disease for accurate forecasts and concrete steps for future diagnosis. The manual analysis and prediction of a massive volume of data are challenging and time-consuming. In this paper, a unique heart disease prediction model is proposed to predict heart disease correctly and rapidly using a variety of bodily signs. A heart disease prediction algorithm based on the analysis of the predictive models’ classification performance on combined datasets and the train-test split technique is presented. Finally, the proposed technique’s training results are compared with the previous works. For the Cleveland, Switzerland, Hungarian, and Long Beach VA heart disease datasets, accuracy, precision, recall, F1-score, and ROC-AUC curves are used as the performance indicators. The analytical outcomes for Random Forest Classifiers (RFC) of the combined heart disease datasets are F1-score 100%, accuracy 100%, precision 100%, recall 100%, and the ROC-AUC 100%. The Decision Tree Classifiers for pooled heart disease datasets are F1-score 100%, accuracy 98.80%, precision 98%, recall 99%, ROC-AUC 99%, and for RFC and Gradient Boosting Classifiers (GBC), the ROC-AUC gives 100% performance. The performances of the machine learning algorithms are improved by using five-fold cross validation. Again, the Stacking CV Classifier is also used to improve the performances of the individual machine learning algorithms by combining two and three techniques together. In this paper, several reduction methods are incorporated. It is found that the accuracy of the RFC classification algorithm is high. Moreover, the developed method is efficient and reliable for predicting heart disease. |
| first_indexed | 2024-03-09T05:37:27Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T05:37:27Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1f01e8b5f9ea4c398fc0f6f86197b2442023-12-03T12:27:17ZengMDPI AGApplied Sciences2076-34172022-07-011215744910.3390/app12157449Mixed Machine Learning Approach for Efficient Prediction of Human Heart Disease by Identifying the Numerical and Categorical FeaturesGhulab Nabi Ahmad0Shafiullah1Hira Fatima2Mohamed Abbas3Obaidur Rahman4Imdadullah5Mohammed S. Alqahtani6Institute of Applied Sciences, Mangalayatan University, Aligarh 202145, IndiaDepartment of Mathematics, K.C.T.C. College, Raxaul, BRA, Bihar University, Muzaffarpur 842001, IndiaInstitute of Applied Sciences, Mangalayatan University, Aligarh 202145, IndiaElectrical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi ArabiaDepartment of Electrical Engineering, Jamia Millia Islamia, New Delhi 110025, IndiaElectrical Engineering Section, University Polytechnic, Aligarh Muslim University, Aligarh 202002, IndiaRadiological Sciences Department, College of Applied Medical Sciences, King Khalid University, Abha 61421, Saudi ArabiaHeart disease is a danger to people’s health because of its prevalence and high mortality risk. Predicting cardiac disease early using a few simple physical indications collected from a routine physical examination has become difficult. Clinically, it is critical and sensitive for the signs of heart disease for accurate forecasts and concrete steps for future diagnosis. The manual analysis and prediction of a massive volume of data are challenging and time-consuming. In this paper, a unique heart disease prediction model is proposed to predict heart disease correctly and rapidly using a variety of bodily signs. A heart disease prediction algorithm based on the analysis of the predictive models’ classification performance on combined datasets and the train-test split technique is presented. Finally, the proposed technique’s training results are compared with the previous works. For the Cleveland, Switzerland, Hungarian, and Long Beach VA heart disease datasets, accuracy, precision, recall, F1-score, and ROC-AUC curves are used as the performance indicators. The analytical outcomes for Random Forest Classifiers (RFC) of the combined heart disease datasets are F1-score 100%, accuracy 100%, precision 100%, recall 100%, and the ROC-AUC 100%. The Decision Tree Classifiers for pooled heart disease datasets are F1-score 100%, accuracy 98.80%, precision 98%, recall 99%, ROC-AUC 99%, and for RFC and Gradient Boosting Classifiers (GBC), the ROC-AUC gives 100% performance. The performances of the machine learning algorithms are improved by using five-fold cross validation. Again, the Stacking CV Classifier is also used to improve the performances of the individual machine learning algorithms by combining two and three techniques together. In this paper, several reduction methods are incorporated. It is found that the accuracy of the RFC classification algorithm is high. Moreover, the developed method is efficient and reliable for predicting heart disease.https://www.mdpi.com/2076-3417/12/15/7449heart diseasemixed machine learning techniquesnumerical featurescategorical featuresRFCDT |
| spellingShingle | Ghulab Nabi Ahmad Shafiullah Hira Fatima Mohamed Abbas Obaidur Rahman Imdadullah Mohammed S. Alqahtani Mixed Machine Learning Approach for Efficient Prediction of Human Heart Disease by Identifying the Numerical and Categorical Features Applied Sciences heart disease mixed machine learning techniques numerical features categorical features RFC DT |
| title | Mixed Machine Learning Approach for Efficient Prediction of Human Heart Disease by Identifying the Numerical and Categorical Features |
| title_full | Mixed Machine Learning Approach for Efficient Prediction of Human Heart Disease by Identifying the Numerical and Categorical Features |
| title_fullStr | Mixed Machine Learning Approach for Efficient Prediction of Human Heart Disease by Identifying the Numerical and Categorical Features |
| title_full_unstemmed | Mixed Machine Learning Approach for Efficient Prediction of Human Heart Disease by Identifying the Numerical and Categorical Features |
| title_short | Mixed Machine Learning Approach for Efficient Prediction of Human Heart Disease by Identifying the Numerical and Categorical Features |
| title_sort | mixed machine learning approach for efficient prediction of human heart disease by identifying the numerical and categorical features |
| topic | heart disease mixed machine learning techniques numerical features categorical features RFC DT |
| url | https://www.mdpi.com/2076-3417/12/15/7449 |
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