Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets
Heart disease is one of the primary causes of morbidity and death worldwide. Millions of people have had heart attacks every year, and only early-stage predictions can help to reduce the number. Researchers are working on designing and developing early-stage prediction systems using different advanc...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2024-03-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-1917.pdf |
| _version_ | 1797254453825372160 |
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| author | Mahmudul Hasan Md Abdus Sahid Md Palash Uddin Md Abu Marjan Seifedine Kadry Jungeun Kim |
| author_facet | Mahmudul Hasan Md Abdus Sahid Md Palash Uddin Md Abu Marjan Seifedine Kadry Jungeun Kim |
| author_sort | Mahmudul Hasan |
| collection | DOAJ |
| description | Heart disease is one of the primary causes of morbidity and death worldwide. Millions of people have had heart attacks every year, and only early-stage predictions can help to reduce the number. Researchers are working on designing and developing early-stage prediction systems using different advanced technologies, and machine learning (ML) is one of them. Almost all existing ML-based works consider the same dataset (intra-dataset) for the training and validation of their method. In particular, they do not consider inter-dataset performance checks, where different datasets are used in the training and testing phases. In inter-dataset setup, existing ML models show a poor performance named the inter-dataset discrepancy problem. This work focuses on mitigating the inter-dataset discrepancy problem by considering five available heart disease datasets and their combined form. All potential training and testing mode combinations are systematically executed to assess discrepancies before and after applying the proposed methods. Imbalance data handling using SMOTE-Tomek, feature selection using random forest (RF), and feature extraction using principle component analysis (PCA) with a long preprocessing pipeline are used to mitigate the inter-dataset discrepancy problem. The preprocessing pipeline builds on missing value handling using RF regression, log transformation, outlier removal, normalization, and data balancing that convert the datasets to more ML-centric. Support vector machine, K-nearest neighbors, decision tree, RF, eXtreme Gradient Boosting, Gaussian naive Bayes, logistic regression, and multilayer perceptron are used as classifiers. Experimental results show that feature selection and classification using RF produce better results than other combination strategies in both single- and inter-dataset setups. In certain configurations of individual datasets, RF demonstrates 100% accuracy and 96% accuracy during the feature selection phase in an inter-dataset setup, exhibiting commendable precision, recall, F1 score, specificity, and AUC score. The results indicate that an effective preprocessing technique has the potential to improve the performance of the ML model without necessitating the development of intricate prediction models. Addressing inter-dataset discrepancies introduces a novel research avenue, enabling the amalgamation of identical features from various datasets to construct a comprehensive global dataset within a specific domain. |
| first_indexed | 2024-04-24T21:50:12Z |
| format | Article |
| id | doaj.art-819c1deb9a8d48c9a81bb620c5cb2f91 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-04-24T21:50:12Z |
| publishDate | 2024-03-01 |
| publisher | PeerJ Inc. |
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| series | PeerJ Computer Science |
| spelling | doaj.art-819c1deb9a8d48c9a81bb620c5cb2f912024-03-20T15:05:10ZengPeerJ Inc.PeerJ Computer Science2376-59922024-03-0110e191710.7717/peerj-cs.1917Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasetsMahmudul Hasan0Md Abdus Sahid1Md Palash Uddin2Md Abu Marjan3Seifedine Kadry4Jungeun Kim5Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, BangladeshDepartment of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, BangladeshDepartment of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, BangladeshDepartment of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, BangladeshDepartment of Electrical and Computer Engineering, Lebanese American University, Byblos, LebanonDepartment of Software, Kongju National University, Cheonan, Republic of South KoreaHeart disease is one of the primary causes of morbidity and death worldwide. Millions of people have had heart attacks every year, and only early-stage predictions can help to reduce the number. Researchers are working on designing and developing early-stage prediction systems using different advanced technologies, and machine learning (ML) is one of them. Almost all existing ML-based works consider the same dataset (intra-dataset) for the training and validation of their method. In particular, they do not consider inter-dataset performance checks, where different datasets are used in the training and testing phases. In inter-dataset setup, existing ML models show a poor performance named the inter-dataset discrepancy problem. This work focuses on mitigating the inter-dataset discrepancy problem by considering five available heart disease datasets and their combined form. All potential training and testing mode combinations are systematically executed to assess discrepancies before and after applying the proposed methods. Imbalance data handling using SMOTE-Tomek, feature selection using random forest (RF), and feature extraction using principle component analysis (PCA) with a long preprocessing pipeline are used to mitigate the inter-dataset discrepancy problem. The preprocessing pipeline builds on missing value handling using RF regression, log transformation, outlier removal, normalization, and data balancing that convert the datasets to more ML-centric. Support vector machine, K-nearest neighbors, decision tree, RF, eXtreme Gradient Boosting, Gaussian naive Bayes, logistic regression, and multilayer perceptron are used as classifiers. Experimental results show that feature selection and classification using RF produce better results than other combination strategies in both single- and inter-dataset setups. In certain configurations of individual datasets, RF demonstrates 100% accuracy and 96% accuracy during the feature selection phase in an inter-dataset setup, exhibiting commendable precision, recall, F1 score, specificity, and AUC score. The results indicate that an effective preprocessing technique has the potential to improve the performance of the ML model without necessitating the development of intricate prediction models. Addressing inter-dataset discrepancies introduces a novel research avenue, enabling the amalgamation of identical features from various datasets to construct a comprehensive global dataset within a specific domain.https://peerj.com/articles/cs-1917.pdfInter-datasetPerformance discrepancyDimensionality reductionHeart disease predictionMachine learning |
| spellingShingle | Mahmudul Hasan Md Abdus Sahid Md Palash Uddin Md Abu Marjan Seifedine Kadry Jungeun Kim Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets PeerJ Computer Science Inter-dataset Performance discrepancy Dimensionality reduction Heart disease prediction Machine learning |
| title | Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets |
| title_full | Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets |
| title_fullStr | Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets |
| title_full_unstemmed | Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets |
| title_short | Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets |
| title_sort | performance discrepancy mitigation in heart disease prediction for multisensory inter datasets |
| topic | Inter-dataset Performance discrepancy Dimensionality reduction Heart disease prediction Machine learning |
| url | https://peerj.com/articles/cs-1917.pdf |
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