Authors: Manpreet Kaur; Shalli Rani; Deepali Gupta; Amit Kumar Manocha
Addresses: Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India ' Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India ' Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India ' Department of Electrical Engineering, Punjab Institute of Technology (GTB Garh, Moga), Punjab, India
Abstract: Growth of technology and digitisation of several areas has made the world more successful in reaching to the solutions of the remote problems. Large amount of health records is also available in digital storage. Machine learning plays an important role for uncovering the health issues from the digital records or for diagnosis of various diseases. In this paper, we are presenting the basic introduction of Recommender System (RS) with respect to diabetic patients after the rigorous review of already present literature. An experiment analysis is performed in Python with the help of machine learning classifiers such as Logistic Regression, Averaged Perception, Bayes Point, Boosted Decision Tree, Neural Network, Decision Forest, Two-Class Support Vector Machine and Locally Deep SVM on Pima Indian Diabetes Database. We conducted an experiment on 23 K diabetic patients' data set. Based on the all classifiers results, it reveals the Logistic Regression performs best over all other classifiers with an accuracy of 78% and predicting the accurate results in specificity of 92%.
Keywords: recommender system; collaborative filtering; diabetic patients; diabetic mellitus; machine learning.
International Journal of Computer Applications in Technology, 2021 Vol.66 No.2, pp.100 - 106
Received: 28 May 2020
Accepted: 13 Jul 2020
Published online: 14 Dec 2021 *