Universal Journal of Public Health Vol. 9(6), pp. 460 - 471
DOI: 10.13189/ujph.2021.090614
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Female Diabetic Prediction in India Using Different Learning Algorithms

Payal Bose ¹, Samir Kumar Bandyopadhyay ^1,*, Amiya Bhaumik ², Sandeep Poddar ^3
1 Faculty of Engineering, Lincoln University College, Malaysia
² Faculty of Business Administration, Lincoln University College, Malaysia
³ Faculty of Research & Innovation, Lincoln University College, Malaysia

ABSTRACT

Diabetes, also known as Diabetic Mellitus, is a metabolic disease that affects the body's natural blood glucose levels. It is a non-contagious illness with numerous serious health risks. The said illness is rapidly growing in India. It is a chronic disorder that happens when the human body unable to create enough insulin hormone to keep blood sugar levels under control. Several characteristics that cause diabetes were investigated in this study, and multiple machine learning techniques were used to predict whether or not an unknown substance had diabetes. PIMA diabetes detection for female patients was employed for this purpose. For prediction, six distinct classification models were applied. This research presented a comprehensive performance assessment of the multiple factors in the PIMA dataset. Based on all factors of the PIMA dataset, a full discussion was made to demonstrate how diabetes is affected. Finally, in order to forecast the best automated diabetic prediction model, a thorough analysis of many classification approaches was undertaken. It was discovered that the Support Vector Machine (SVM) model delivers the best prediction result, with a reliability of 83.5 percent. Interestingly, Random Forest (RF) Classifier produced the second-best prediction result, with a reliability of 82.76 percent. The study's findings demonstrate that machine learning models produce efficient solutions. The accuracy of the two best machine learning models is 82-83 percent, which can be used for subsequent improvement of the autonomous forecasting tool. The accuracy of these techniques can be improved further by integrating diverse variables for prediction and classification.

KEYWORDS
Diabetics, Diabetic Mellitus, Diabetic Prediction, Female Diabetic Patients, Machine Learning(ML), PIMA Diabetic Dataset, Support Vector Machine, Random Forest

Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] Payal Bose , Samir Kumar Bandyopadhyay , Amiya Bhaumik , Sandeep Poddar , "Female Diabetic Prediction in India Using Different Learning Algorithms," Universal Journal of Public Health, Vol. 9, No. 6, pp. 460 - 471, 2021. DOI: 10.13189/ujph.2021.090614.

(b). APA Format:
Payal Bose , Samir Kumar Bandyopadhyay , Amiya Bhaumik , Sandeep Poddar (2021). Female Diabetic Prediction in India Using Different Learning Algorithms. Universal Journal of Public Health, 9(6), 460 - 471. DOI: 10.13189/ujph.2021.090614.