1Dr T K Ganga, 2Dr S Selvakani, 3Mrs K Vasumathi
1Associate Professor Muthurangam Government Arts College (A), Vellore
1,2Assistant Professor, Department of Computer Science, Government Arts and Science College, Arakkonam.
DOI : https://doi.org/10.47191/ijmra/v6-i12-67Google Scholar Download Pdf
ABSTRACT:
Diabetes is recognized as a severe and persistent illness that leads to an elevation in blood sugar levels. Untreated and undetected diabetes can give rise to numerous complications. The laborious process of identification typically involves a patient visiting a diagnostic centre and consulting with a doctor. However, the advancement of machine learning approaches has addressed this critical issue. The objective of this study is to develop a model capable of predicting the likelihood of diabetes in patients with the utmost accuracy. To achieve this goal, three machine learning classification algorithms—Decision Tree, SVM, and Naive Bayes—are employed in this experiment to identify diabetes at an early stage. The experiments are conducted on the Pima Indians Diabetes Database (PIDD), sourced from the UCI machine learning repository. The performance of each algorithm is assessed using various metrics such as Precision, Accuracy, F-Measure, and Recall. Accuracy is gauged based on both correctly and incorrectly classified instances. The results indicate that Naive Bayes outperforms the other algorithms, achieving the highest accuracy of 76.30%. These findings are corroborated through a meticulous examination of Receiver Operating Characteristic (ROC) curves.
KEYWORDS:Decision Tree Algorithm, Machine Learning, Prediction, Diabetes, Pima Indians Diabetes Database
REFERENCES1) Aishwarya, R., Gayathri, P., Jaisankar, N., 2013. A Method for Classification Using Machine Learning Technique for Diabetes. International Journal of Engineering and Technology (IJET) 5, 2903–2908.
2) Aljumah, A.A., Ahamad, M.G., Siddiqui, M.K., 2013. Application of data mining: Diabetes health care in young and old patients. Journal of King Saud University - Computer and Information Sciences 25, 127–136.
3) Arora, R., Suman, 2012. Comparative Analysis of Classification Algorithms on Different Datasets using WEKA. International Journal of Computer Applications 54, 21–25.
4) Bamnote, M.P., G.R., 2014. Design of Classifier for Detection of Diabetes Mellitus Using Genetic Programming. Advances in Intelligent Systems and Computing 1, 763–770.
5) Choubey, D.K., Paul, S., Kumar, S., Kumar, S., 2017. Classification of Pima indian diabetes dataset using naive bayes with genetic algorithm as an attribute selection, in: Communication and Computing Systems: Proceedings of the International Conference on Communication and Computing System (ICCCS 2016), pp. 451–455.
6) Dhomse Kanchan B., M.K.M., 2016. Study of Machine Learning Algorithms for Special Disease Prediction using Principal of Component Analysis, in: 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication, IEEE. pp. 5–10.
7) Esposito, F., Malerba, D., Semeraro, G., Kay, J., 1997. A comparative analysis of methods for pruning decision trees. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 476–491.
8) Fatima, M., Pasha, M., 2017. Survey of Machine Learning Algorithms for Disease Diagnostic. Journal of Intelligent Learning Systems and Applications 09, 1–16.
9) Garner, S.R., 1995. Weka: The Waikato Environment for Knowledge Analysis, in: Proceedings of the New Zealand computer science research students conference, Citeseer. pp. 57–64.
10) Han, J., Rodriguez, J.C., Beheshti, M., 2008. Discovering decision tree based diabetes prediction model, in: International Conference on Advanced Software Engineering and Its Applications, Springer. pp. 99–109.
11) Kumar, D.A., Govindasamy,R., 2015. Performance and Evaluation of Classification Data Mining Techniques in Diabetes. International Journal of Computer Science and Information Technologies, 6, 1312–1319.
12) Kumar,P.S., Umatejaswi,V., 2017. Diagnosing Diabetes using Data Mining Techniques. International Journal of Scientific and Research Publications 7, 705–709.
13) Kumari,V.A., Chitra,R.,2013. Classification Of Diabetes Disease Using Support Vector Machine. International Journal of Engineering Research and Applications (IJERA 1797–1801.
14) Nai-Arun,N., Moungmai,R. ,2015. Comparison of Classifiers for the Risk of Diabetes Prediction. Procedia Computer Science 69,132–142.
15) Nai-Arun,N., Sittidech,P., 2014. Ensemble Learning Model for Diabetes Classification. Advanced Materials Research 931-932, 1427–1431.
16) Orabi,K.M., Kamal,Y.M., Rabah,T.M., 2016. Early Predictive System for Diabetes Mellitus Disease, in: Industrial Conference on Data Mining, Springer.pp.420–427
17) Pradhan, P.M.A., Bamnote, G.R.,Tribhuvan, V.,Jadhav, K.,Chabukswar, V.,Dhobale,V., 2012. A Genetic Programming Approach for Detection of Diabetes. International Journal Of Computational Engineering Research 2, 91–94.
18) Sharief,A.A., Sheta,A.,2014. Developing a Mathematical Model to Detect Diabetes Using Multigene Genetic Programming. International Journal of Advanced Research in Artificial Intelligence (IJARAI) 3, 54–59.
19) TarikA. Rashid,S. M.A.,Abdullah, R.M.,Abstract, 2016. An Intelligent Approach for Diabetes Classification, Prediction and Description. Advances in Intelligent Systems and Computing 424, 323–335.
Volume 06 Issue 12 December 2023
There is an Open Access article, distributed under the term of the Creative Commons Attribution – Non Commercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/), which permits remixing, adapting and building upon the work for non-commercial use, provided the original work is properly cited.
Our Services and Policies
Authors should prepare their manuscripts according to the instructions given in the authors' guidelines. Manuscripts which do not conform to the format and style of the Journal may be returned to the authors for revision or rejected.
The Journal reserves the right to make any further formal changes and language corrections necessary in a manuscript accepted for publication so that it conforms to the formatting requirements of the Journal.
International Journal of Multidisciplinary Research and Analysis will publish 12 monthly online issues per year,IJMRA publishes articles as soon as the final copy-edited version is approved. IJMRA publishes articles and review papers of all subjects area.
Open access is a mechanism by which research outputs are distributed online, Hybrid open access journals, contain a mixture of open access articles and closed access articles.
International Journal of Multidisciplinary Research and Analysis initiate a call for research paper for Volume 07 Issue 05 (May 2024).
PUBLICATION DATES:
1) Last Date of Submission : 26 May 2024 .
2) Article published within a week.
3) Submit Article : editor@ijmra.in or Online
Why with us
1 : IJMRA only accepts original and high quality research and technical papers.
2 : Paper will publish immediately in current issue after registration.
3 : Authors can download their full papers at any time with digital certificate.
The Editors reserve the right to reject papers without sending them out for review.
Authors should prepare their manuscripts according to the instructions given in the authors' guidelines. Manuscripts which do not conform to the format and style of the Journal may be returned to the authors for revision or rejected. The Journal reserves the right to make any further formal changes and language corrections necessary in a manuscript accepted for publication so that it conforms to the formatting requirements of the Journal.