Diabetes Mellitus Classification Using Machine Learning Algorithms with Hyperparameter Tuning
subject: Diabetes Mellitus; GridSearchCV; Hyperparameter Tuning; Machine Learning; One-hot Encoding; Random Forest
description: Diabetes Mellitus is a prevalent condition globally, marked by elevated blood sugar levels resulting from either insufficient production of insulin or the body cells' inability to respond appropriately to released insulin. For people with diabetes to lead healthy, normal lives, early identification and treatment of the condition are essential. With the need to move away from current traditional procedures, towards a noninvasive methodology, machine learning and data mining technologies can be very useful in the classification of diabetes. Creating an effective machine learning model for the classification of diabetes mellitus was the primary goal of this research. This work is primarily carried out on combined Pima Indian diabetes dataset and German Frankfurt diabetes dataset. The class imbalance issue has been resolved using Synthetic Minority Oversampling Technique. One-hot encoding is applied to convert categorial features to numerical and various single and ensemble classifiers with the best hyperparameters obtained using GridSearchCV method were employed on the pre-processed dataset. With an AUC of 0.98 and maximum accuracy of 98.79%, the Random Forest ensemble technique outperformed the other models, according to the experimental results. As a result, the algorithm might be used to predict diabetes and alert doctors to serious cases that call for emergency care. 2024 IEEE.
creator: Karna V.V.R.; Karna V.R.; Beemagani R.; Tummala A.B.; Arigela S.V.; Janamala V.; Flah A.
source: 2024 6th International Symposium on Advanced Electrical and Communication Technologies, ISAECT 2024
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-833152998-7
format: Online
language: English
type: Conference paper
coverage: Karna V.V.R., Aditya University, Electronics and Communication Engineeirng, Surampalem, India; Karna V.R., R V College of Engineering, Department of Aiml, Bengaluru, India; Beemagani R., Indian Institute of Technology, Dept. of Electrical Engineering, Tirupati, India; Tummala A.B., Chaitanya Bharati Institute of Technology, Electronics and Communication Engineeirng, Hyderabad, India; Arigela S.V., Manipal Academy of Higher Education, Manipal Institute of Technology, Department of Electrical and Electronics Engineering, Manipal, 576104, India; Janamala V., Christ University, School of Engineering and Technology, Dept. of Electrical and Electronics Engineering, Karnataka, Bangalore, 560074, India; Flah A., Energy Processes Environment and Electrical Systems Unit, National Engineering School of Gabes, University of Gabes, Tunis, Gab, 6029, Tunisia, Middle East University, Meu Research Unit, Amman, 11831, Jordan, University of Gabes, Private Higher School of Applied Sciences and Technology of Gabes, Gab, 6029, Tunisia, Vsb - Technical University of Ostrava, Enet Centre, Ostrava, 708 00, Czech Republic, Applied Science Private University, Applied Science Research Center, Amman, 11931, Jordan