Enhancement of Classifier Performance Using Swarm Intelligence in Detection of Diabetes from Pancreatic Microarray Gene Data

• Published in: Biomimetics (Basel, Switzerland) Vol. 8; no. 6; p. 503
• Main Authors: Chellappan, Dinesh, Rajaguru, Harikumar
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2023; MDPI
• Subjects: Accuracy; Algae; Algorithms; Bayesian analysis; Blood pressure; Cardiovascular disease; Classification; classification techniques; Datasets; Decision trees; Diabetes; Diabetes mellitus; Diagnosis; dimensionality reduction (DR); Discriminant analysis; Disease prevention; DNA microarrays; Feature selection; Gene expression; Genetic aspects; Hypertension; Laboratories; Literature reviews; Machine learning; Medical diagnosis; Metabolic disorders; microarray gene data; Nutrition; Obesity; Pancreas; Performance evaluation; Regression analysis; Swarm intelligence; type II DM; India
• ISSN: 2313-7673; 2313-7673
• DOI: 10.3390/biomimetics8060503

In this study, we focused on using microarray gene data from pancreatic sources to detect diabetes mellitus. Dimensionality reduction (DR) techniques were used to reduce the dimensionally high microarray gene data. DR methods like the Bessel function, Discrete Cosine Transform (DCT), Least Squares Linear Regression (LSLR), and Artificial Algae Algorithm (AAA) are used. Subsequently, we applied meta-heuristic algorithms like the Dragonfly Optimization Algorithm (DOA) and Elephant Herding Optimization Algorithm (EHO) for feature selection. Classifiers such as Nonlinear Regression (NLR), Linear Regression (LR), Gaussian Mixture Model (GMM), Expectation Maximum (EM), Bayesian Linear Discriminant Classifier (BLDC), Logistic Regression (LoR), Softmax Discriminant Classifier (SDC), and Support Vector Machine (SVM) with three types of kernels, Linear, Polynomial, and Radial Basis Function (RBF), were utilized to detect diabetes. The classifier’s performance was analyzed based on parameters like accuracy, F1 score, MCC, error rate, FM metric, and Kappa. Without feature selection, the SVM (RBF) classifier achieved a high accuracy of 90% using the AAA DR methods. The SVM (RBF) classifier using the AAA DR method for EHO feature selection outperformed the other classifiers with an accuracy of 95.714%. This improvement in the accuracy of the classifier’s performance emphasizes the role of feature selection methods.