Affiliations: [a] Department of Computer Science, University of Uyo, Uyo, Akwa Ibom State, Nigeria | [b] TEFTFund Centre of Excellence in Computational Intelligence Research, University of Uyo, Nigeria | [c] School of Management, National Institute of Technology Karnataka, Surathkal, India
Correspondence:
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Corresponding author: Uduak Umoh, Department of Computer Science, University of Uyo, PMB 1017, Uyo, Akwa Ibom State, Nigeria. E-mail: [email protected].
Abstract: This paper compares the effectiveness of Interval Type 2 Fuzzy Logic (IT2FL) and Machine Learning (ML) models in addressing real-world challenges. It employs four non-parametric ML algorithms (Support Vector Machine (SVM), K-Nearest Neighbor (KNN), (Random Forest (RF) and Classification and Regression Tree (CART)) and evaluates their performance against IT2FL using parameters generated through Gaussian membership functions. Initially, the IT2FL algorithm preprocesses the dataset by predicting missing values, leveraging IT2F membership to optimize decision-making and mitigate uncertainties. The study assesses the predictive performance, robustness, and interpretability of IT2F-ML models, analyzing datasets from cardiovascular disease patients to predict shock levels. After transforming the dataset using IT2FL, it is divided into 60% training and 40% testing sets to train and test four ML algorithms, aimed at predicting shock levels in patients. The models’ performance is evaluated using various metrics, demonstrating the superior learning enhancement and performance of the IT2F-SVR method compared to other predictive approaches on the same dataset. Moreso, it is observed that the integration of the IT2-FL algorithm with machine learning models offers a promising approach for addressing data incompleteness and improving prediction accuracy. Furthermore, the study provides valuable guidance for researchers and practitioners in selecting suitable methodologies for prediction tasks.
