Risk Assessment of Diabetes Mellitus Dataset Using Supervised Machine Learning Algorithms
Volume 18, Issue 1, 2024
Download| Author(s): |
Nehl Roop Ziauddin University, Faculty of Engineering, Science, Technology & Management, nehl13477@zu.edu.pk Munaf Rashid Ziauddin University, Faculty of Engineering, Science, Technology & Management, munaf.rashid@zu.edu.pk Sidra Abid Syed Sir Syed University of Engineering & Technology, sidra.agha@yahoo.com Fahad Shamim Institute of Biomedical Engineering and Technology (IBET), LUMHS, Jamshoro Sindh, fahad.shamim@lumhs.edu.pk Sarmad Shams* Institute of Biomedical Engineering and Technology (IBET), LUMHS, Jamshoro Sindh, sarmad.shams@lumhs.edu.pk Shahzad Nasim The Begum Nusrat Bhutto Women University, Sukkur, shahzadnasim@live.com |
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| Abstract | Diabetes mellitus is a chronic condition that can lead to serious health complications if not properly managed and this research paper focuses on its early diagnosis and risk assessment Machine Learning Support Vector and Machine Learning Random Forest are the two algorithms being used in this study to provide a comparative analysis of their predictive accuracies and efficiency. The research is conducted using a multivariate dataset, consisting of 520 instances and 17 attributes, obtained from the UCI Repository (Machine Learning). After thorough analysis, it is found that both SVM and Random Forest algorithms performs well in predicting diabetes mellitus risk. However, comparison of the accuracies of both algorithms shows that the RF classifier yielded greater accuracy and provided the most suitable output. This study is an effective demonstration of the importance and effectiveness for the early diagnosis by utilizing artificially learned patterns and risk assessment of diseases like diabetes mellitus. The findings also highlight the significance of comparative analysis to identify the most accurate and efficient algorithm for a given dataset. |
| Keywords | Early diagnosis, Risk assessment, Diabetes mellitus, SVM, Random Forest, Multivariate dataset, UCI Machine Learning Repository |
| Year | 2024 |
| Volume | 18 |
| Issue | 1 |
| Type | Research paper, manuscript, article |
| Journal Name | Journal of Information & Communication Technology | Publisher Name | ILMA University | Jel Classification | - | DOI | - | ISSN no (E, Electronic) | 2075-7239 | ISSN no (P, Print) | 2415-0169 | Country | Pakistan | City | Karachi | Institution Type | University | Journal Type | Open Access | Manuscript Processing | Blind Peer Reviewed | Format | Paper Link | https://jict.ilmauniversity.edu.pk/journal/jict/18.1/3.pdf | Page |