Version 1
: Received: 3 June 2021 / Approved: 4 June 2021 / Online: 4 June 2021 (15:25:16 CEST)
How to cite:
Bandyopadhyay, S.; Bose, P.; Goyel, V. Prediction of Female Diabetic Patient in India Using Different Learning Algorithms. Preprints2021, 2021060144. https://doi.org/10.20944/preprints202106.0144.v1
Bandyopadhyay, S.; Bose, P.; Goyel, V. Prediction of Female Diabetic Patient in India Using Different Learning Algorithms. Preprints 2021, 2021060144. https://doi.org/10.20944/preprints202106.0144.v1
Bandyopadhyay, S.; Bose, P.; Goyel, V. Prediction of Female Diabetic Patient in India Using Different Learning Algorithms. Preprints2021, 2021060144. https://doi.org/10.20944/preprints202106.0144.v1
APA Style
Bandyopadhyay, S., Bose, P., & Goyel, V. (2021). Prediction of Female Diabetic Patient in India Using Different Learning Algorithms. Preprints. https://doi.org/10.20944/preprints202106.0144.v1
Chicago/Turabian Style
Bandyopadhyay, S., Payal Bose and Vishal Goyel. 2021 "Prediction of Female Diabetic Patient in India Using Different Learning Algorithms" Preprints. https://doi.org/10.20944/preprints202106.0144.v1
Abstract
Diabetics or Diabetic Mellitus is a metabolic disorder of blood sugar levels in the human body. It is a major non-communicable disease and involved many serious health risk issues. This disease is rapidly increasing in India. It is a chronic condition and it occurs when a body doesn't produce enough insulin hormone to control the blood sugar level. In this study, different variables have been analyzed that cause the diabetics, and different machine learning algorithms are used to predict whether an unknown sample is diabetes or not. For this purpose, PIMA diabetic detection for Female patients was used. Here 10 different classification model is used for prediction. Finally, the detailed performance analysis of the different variables of the PIMA dataset and also the classification model are discussed.
Medicine and Pharmacology, Endocrinology and Metabolism
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
