An Intelligent Harris Hawks Optimization (IHHO) based Pivotal Decision Tree (PDT) Machine Learning Model for Diabetes Prediction

Authors

  • Roma Fayaz Lecturer, Department of Computer Science, College of Computer Science and Information Technology, Jazan University, Jazan, Kingdom of Saudi Arabia
  • G.Vinoda Reddy Professor, Department of Computer Science and Engineering (AI&ML), CMR Technical Campus, Kandlakoya, Medchal (M), Hyderabad, Telangana-501401
  • M. Sujaritha Professor,Sri Krishna College of Engineering and Technology, Kuniyamuthur, Tamil Nadu 641008, India
  • N. Soundiraraj Assistant Professor Department of Electronics and Communication Engineering, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, 624622, India
  • W.Gracy Theresa Associate Professor, Department of Computer and Science Engineering, Panimalar Engineering College, Chennai, Tamil Nadu 600123, India
  • Dharmendra Kumar Roy Assistant Professor, Department of Computer Science and Engineering, Hyderabad Institute of Technology and Management (HITAM), Hyderabad, Telangana, 501401,India
  • J.Jeffin Gracewell Assistant Professor, Department of Electronics and Communication, Saveetha Engineering College, Chennai, Tamil Nadu 602105,India
  • S. Gopalakrishnan Associate Professor, Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology (Deemed To be University),Chennai-600062, Tamil Nadu, India

Keywords:

Diabetes Prediction, Machine Learning, Inherent Coefficient Normalization (ICN), Intelligent Harris Hawks Optimization (IHHO), Pivotal Decision Tree (PDT), PIMA Indian Dataset

Abstract

In ancient times, an accurate diabetes prediction and type of classification are the most important and demanding tasks in the medical field for providing proper diagnosis to the patients. For this purpose, various machine learning based detection systems are developed in the conventional works to predict the diabetes from the given dataset. Still, it has some limitations with the factors of difficult to understand, high time requirement for training and testing, over fitting, and error outputs. Therefore, the proposed research work objects to implement a group of data mining techniques for developing an automated and efficient diabetes detection system.  In this framework, an Inherent Coefficient Normalization (ICN) technique is implemented at first for preprocessing the PIMA Indian dataset obtained from the repository, which highly improves the quality of data for processing. Then, an Intelligent Harris Hawks Optimization (IHHO) technique is utilized to optimally select the features for training the classifier. Finally, the Pivotal Decision Tree (PDT) based classification technique is deployed to predict the data as whether diabetes or non-diabetes with reduced computational complexity and time consumption. During analysis, the performance and results of the proposed IHHO-PDT technique is validated and compared using various measures.

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Types of data mining techniques used for diabetes prediction

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Published

16.12.2022

How to Cite

Fayaz, R. ., Reddy, G. ., Sujaritha, M., Soundiraraj, N., Theresa, W. ., Roy, D. K. ., Gracewell, J. ., & Gopalakrishnan, S. (2022). An Intelligent Harris Hawks Optimization (IHHO) based Pivotal Decision Tree (PDT) Machine Learning Model for Diabetes Prediction. International Journal of Intelligent Systems and Applications in Engineering, 10(4), 415–423. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2277

Issue

Vol. 10 No. 4 (2022)

Section

Research Article

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