Medical Data Clustering and Classification Using TLBO and Machine Learning Algorithms

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.