Particle Swarm Optimization-Based Random Forest Framework for the Classification of Chronic Diseases

Abstract

In this paper, a hybrid metaheuristic-based Machine learning approach has been propounded for the classification of various Chronic Diseases (CDs). The CDs often get misdiagnosed due to various issues viz., similar and overlapping symptoms, sensitive devices, lack of clinical experts, etc. Based on the above issues, this study has utilized a fusion of Particle Swarm Optimization with Random Forest (PSORF) for the automatic identification of CDs. The approach PSORF comprises of two main components: PSO for obtaining the minimal optimal feature set, also to optimize the performance of the RF classifier, and RF classifier for the classification of multiple CDs. In this research, five different CD datasets have been deployed onto a series of experiments have been conducted to identify the best approach for the classification of CDs. To address the issues of imbalanced and incomplete data in the datasets used, Synthetic Minority Oversampling Technique (SMOTE) and Expected Minimization (EM) Imputation techniques have been applied before training the model. This ensures the data quality is improved before being used for analysis. Furthermore, the performance of the PSO and RF classifiers has been compared with other metaheuristic and ML classifiers in terms of different performance metrics. For this purpose, Friedman's tests have been employed to calculate the mean ranks of all the classifiers across all the datasets for different metrics. The results showed that the proposed technique achieved the highest mean rank in terms of Accuracy, F-measure, and Receiver Operating Characteristics (ROC) across all four datasets.