The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Prediction of Type 2 Diabetes using Machine Learning Classification Methods
Open accessAbstract
Over 30 million people in India are suffering from diabetes and many others are under the risk. Thus, early diagnosis and treatment is required to prevent diabetes and its associated health problems. This study aims to assess the risk of diabetes among individuals based on their lifestyle and family background. The risk of Type 2 diabetes was predicted using different machine learning algorithms as these algorithms are highly accurate which is very much required in the health profession. Once the model will be trained with good accuracy, then individuals can self-assess the risk of diabetes. In order to conduct the experiment, 952 instances have been collected through an online and offline questionnaire including 18 questions related to health, lifestyle and family background. The same algorithms were also applied to the Pima Indian Diabetes database. The performance of Random Forest Classifier is found to be most accurate for both datasets.
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Cites
An assessment of the effectiveness of a random forest classifier for land-cover classification
Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., & Rigol-Sanchez, J. P. (2012) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2011.11.002 · Cited by 2,907
Machine learning and data mining methods in diabetes research
Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2016.12.005 · Cited by 1,445
on behalf of the ICMR–INDIAB Collaborative Study Group, Anjana, R. M., Pradeepa, R., Deepa, M., Datta, M., Sudha, V., Unnikrishnan, R., Bhansali, A., Joshi, S. R., Joshi, P. P., et al. (2011) · Diabetologia · doi:10.1007/s00125-011-2291-5 · Cited by 922
Predicting Diabetes Mellitus With Machine Learning Techniques
Zou, Q., Qu, K., Luo, Y., Yin, D., Ju, Y., & Tang, H. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00515 · Cited by 920
Prediction of Diabetes using Classification Algorithms
Sisodia, D., & Sisodia, D. S. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.05.122 · Cited by 900
The current state of diabetes mellitus in India
Kaveeshwar, S. (2014) · Australasian Medical Journal · doi:10.4066/amj.2014.1979 · Cited by 788
Type 2 diabetes mellitus prediction model based on data mining
Wu, H., Yang, S., Huang, Z., He, J., & Wang, X. (2018) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2017.12.006 · Cited by 393
Diabetes detection using deep learning algorithms
G., S., R., V., & K.P., S. (2018) · ICT Express · doi:10.1016/j.icte.2018.10.005 · Cited by 340
Comparison of three data mining models for predicting diabetes or prediabetes by risk factors
Meng, X., Huang, Y., Rao, D., Zhang, Q., & Liu, Q. (2013) · The Kaohsiung Journal of Medical Sciences · doi:10.1016/j.kjms.2012.08.016 · Cited by 286
The Metabolic Syndrome in Men study: a resource for studies of metabolic and cardiovascular diseases
Laakso, M., Kuusisto, J., Stančáková, A., Kuulasmaa, T., Pajukanta, P., Lusis, A. J., Collins, F. S., Mohlke, K. L., & Boehnke, M. (2017) · Journal of Lipid Research · doi:10.1194/jlr.o072629 · Cited by 204
Cited by
Kumar, Y., Koul, A., Singla, R., & Ijaz, M. F. (2023) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03612-z · Cited by 1,040
RetractedEvaluation of artificial intelligence techniques in disease diagnosis and prediction
Ghaffar Nia, N., Kaplanoglu, E., & Nasab, A. (2023) · Discover Artificial Intelligence · doi:10.1007/s44163-023-00049-5 · Cited by 426
Prediction of Type 2 Diabetes Based on Machine Learning Algorithm
Deberneh, H. M., & Kim, I. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18063317 · Cited by 261
Hasan, R., Dattana, V., Mahmood, S., & Hussain, S. (2024) · Information · doi:10.3390/info16010007 · Cited by 44
Islam, M. M., Rifat, H. R., Shahid, M. S. B., Akhter, A., Uddin, M. A., & Uddin, K. M. M. (2025) · Engineering Reports · doi:10.1002/eng2.13080 · Cited by 41
Metadata
- Authors
- Tigga, N. P., & Garg, S.
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2020
- Volume
- 167
- Pages
- 706-716
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W3017209650
- Cited by
- 361
- References
- 18
- FWCI
- 42.35
- Type
- conference paper
- Language
- en
- Open access
- diamond
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