Impact of computing platforms on classifier performance in heart disease prediction
Prediction and classification, a supervised learning technique in machine learning, addresses various challenges related to finding useful patterns present in data. This work explores how different computing platforms influence the accuracy of classification results when employing the same models. H...
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| Format: | Article |
| Language: | English |
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Mehran University of Engineering and Technology
2025-04-01
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| Series: | Mehran University Research Journal of Engineering and Technology |
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| Online Access: | https://murjet.muet.edu.pk/index.php/home/article/view/297 |
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| _version_ | 1849761164813139968 |
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| author | Beenish Ayesha Akram Muhammad Irfan Amna Zafar Sidra Khan Rubina Shaheen |
| author_facet | Beenish Ayesha Akram Muhammad Irfan Amna Zafar Sidra Khan Rubina Shaheen |
| author_sort | Beenish Ayesha Akram |
| collection | DOAJ |
| description | Prediction and classification, a supervised learning technique in machine learning, addresses various challenges related to finding useful patterns present in data. This work explores how different computing platforms influence the accuracy of classification results when employing the same models. Heart disease, a widespread global health issue affecting both men and women, results from a complex interplay of lifestyle factors and genetics. Through visual representations, we examined the diverse factors influencing heart stroke occurrences. We employed multiple classification methods such as Logistic Regression, K Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes, and Decision Tree (DT), assessing their accuracy using WEKA and Google Colab (using Scikit-Learn library). Our evaluation revealed that SVM achieves 77% accuracy when implemented using Scikit-Learn, demonstrating superiority over other methods. However, when using WEKA, both logistic regression and SVM demonstrated nearly 91% accuracy using the exact same hyperparameters. This research demonstrated the significance of platform selection in influencing classifier performance, offering valuable insights on how results reported in research can be impacted by the selection of the software and tools, using heart disease prediction as a use case scenario. |
| format | Article |
| id | doaj-art-3c48fe9b5dbd4e06b28fa4da2b528303 |
| institution | DOAJ |
| issn | 0254-7821 2413-7219 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Mehran University of Engineering and Technology |
| record_format | Article |
| series | Mehran University Research Journal of Engineering and Technology |
| spelling | doaj-art-3c48fe9b5dbd4e06b28fa4da2b5283032025-08-20T03:06:06ZengMehran University of Engineering and TechnologyMehran University Research Journal of Engineering and Technology0254-78212413-72192025-04-0144215516310.22581/muet1982.3268299Impact of computing platforms on classifier performance in heart disease predictionBeenish Ayesha Akram0Muhammad Irfan1Amna Zafar2Sidra Khan3Rubina Shaheen4Department of Computer Engineering, University of Engineering and Technology, Lahore, PakistanDepartment of Computer Engineering, University of Engineering and Technology, Lahore, PakistanDepartment of Computer Science, University of Engineering and Technology, Lahore, PakistanDepartment of Computer Engineering, University of Engineering and Technology, Lahore, PakistanDepartment of Computer Engineering, University of Engineering and Technology, Lahore, PakistanPrediction and classification, a supervised learning technique in machine learning, addresses various challenges related to finding useful patterns present in data. This work explores how different computing platforms influence the accuracy of classification results when employing the same models. Heart disease, a widespread global health issue affecting both men and women, results from a complex interplay of lifestyle factors and genetics. Through visual representations, we examined the diverse factors influencing heart stroke occurrences. We employed multiple classification methods such as Logistic Regression, K Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes, and Decision Tree (DT), assessing their accuracy using WEKA and Google Colab (using Scikit-Learn library). Our evaluation revealed that SVM achieves 77% accuracy when implemented using Scikit-Learn, demonstrating superiority over other methods. However, when using WEKA, both logistic regression and SVM demonstrated nearly 91% accuracy using the exact same hyperparameters. This research demonstrated the significance of platform selection in influencing classifier performance, offering valuable insights on how results reported in research can be impacted by the selection of the software and tools, using heart disease prediction as a use case scenario.https://murjet.muet.edu.pk/index.php/home/article/view/297heart disease predictionsupport vector machineclassificationmachine learningclassification metricsplatform comparison |
| spellingShingle | Beenish Ayesha Akram Muhammad Irfan Amna Zafar Sidra Khan Rubina Shaheen Impact of computing platforms on classifier performance in heart disease prediction Mehran University Research Journal of Engineering and Technology heart disease prediction support vector machine classification machine learning classification metrics platform comparison |
| title | Impact of computing platforms on classifier performance in heart disease prediction |
| title_full | Impact of computing platforms on classifier performance in heart disease prediction |
| title_fullStr | Impact of computing platforms on classifier performance in heart disease prediction |
| title_full_unstemmed | Impact of computing platforms on classifier performance in heart disease prediction |
| title_short | Impact of computing platforms on classifier performance in heart disease prediction |
| title_sort | impact of computing platforms on classifier performance in heart disease prediction |
| topic | heart disease prediction support vector machine classification machine learning classification metrics platform comparison |
| url | https://murjet.muet.edu.pk/index.php/home/article/view/297 |
| work_keys_str_mv | AT beenishayeshaakram impactofcomputingplatformsonclassifierperformanceinheartdiseaseprediction AT muhammadirfan impactofcomputingplatformsonclassifierperformanceinheartdiseaseprediction AT amnazafar impactofcomputingplatformsonclassifierperformanceinheartdiseaseprediction AT sidrakhan impactofcomputingplatformsonclassifierperformanceinheartdiseaseprediction AT rubinashaheen impactofcomputingplatformsonclassifierperformanceinheartdiseaseprediction |