Open Access

ARTICLE

Medical Data Clustering and Classification Using TLBO and Machine Learning Algorithms

by Ashutosh Kumar Dubey^1,*, Umesh Gupta², Sonal Jain²

1 Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
2 Institute of Engineering and Technology, JK Lakshmipat University, Jaipur, India

* Corresponding Author: Ashutosh Kumar Dubey. Email:

(This article belongs to the Special Issue: Role of Machine Learning and Evolutionary Algorithms for Cancer Detection and Prediction)

Computers, Materials & Continua 2022, 70(3), 4523-4543. https://doi.org/10.32604/cmc.2022.021148

Received 25 June 2021; Accepted 26 July 2021; Issue published 11 October 2021

Abstract

This study aims to empirically analyze teaching-learning-based optimization (TLBO) and machine learning algorithms using k-means and fuzzy c-means (FCM) algorithms for their individual performance evaluation in terms of clustering and classification. In the first phase, the clustering (k-means and FCM) algorithms were employed independently and the clustering accuracy was evaluated using different computational measures. During the second phase, the non-clustered data obtained from the first phase were preprocessed with TLBO. TLBO was performed using k-means (TLBO-KM) and FCM (TLBO-FCM) (TLBO-KM/FCM) algorithms. The objective function was determined by considering both minimization and maximization criteria. Non-clustered data obtained from the first phase were further utilized and fed as input for threshold optimization. Five benchmark datasets were considered from the University of California, Irvine (UCI) Machine Learning Repository for comparative study and experimentation. These are breast cancer Wisconsin (BCW), Pima Indians Diabetes, Heart-Statlog, Hepatitis, and Cleveland Heart Disease datasets. The combined average accuracy obtained collectively is approximately 99.4% in case of TLBO-KM and 98.6% in case of TLBO-FCM. This approach is also capable of finding the dominating attributes. The findings indicate that TLBO-KM/FCM, considering different computational measures, perform well on the non-clustered data where k-means and FCM, if employed independently, fail to provide significant results. Evaluating different feature sets, the TLBO-KM/FCM and SVM(GS) clearly outperformed all other classifiers in terms of sensitivity, specificity and accuracy. TLBO-KM/FCM attained the highest average sensitivity (98.7%), highest average specificity (98.4%) and highest average accuracy (99.4%) for 10-fold cross validation with different test data.

---

Keywords

---

Citations