TPOT-MLP-SVM: Hybrid Model of Multilayer Perceptron with Support Vector Machines Based on Genetic Programming for Predictive Analysis

Abstract

Nowadays, the integration of machine learning has drawn significant attention due to its effectiveness and robustness. Automated machine learning (AutoML) has transformed the field of artificial intelligence by developing an effective model to solve predictive tasks. Despite the performance of the conventional model, there is a need for improvement to achieve a better and more effective model. In this paper, we proposed TPOT-MLP-SVM, a novel hybrid model to enhance model performance that leverages genetic programming (GP) by integrating multilayer perceptron and support vector machine to improve predictive scores and robustness. The proposed method utilizes GP to automatically search and optimize the structure and parameters of both MLP and SVM variables, thereby minimizing manual input and maximizing predictive performance. Lastly, a real dataset was used to train, test, and validate the proposed model using Python software. The performance was evaluated based on accuracy, precision, sensitivity, specificity, f1-score, and ROC-AUC. Experimental results on benchmark datasets demonstrate the effectiveness and better performance of TPOT-MLP-SVM over stand-alone MLP and SVM models and other hybrid techniques. Overall, TPOT-MLP-SVM is a potential tool for predictive analysis, as it integrates MLP and SVM in a single system guided by genetic programming for early medical diagnosis in healthcare.