Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization

Abstract

With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers have been proposed and tested, and the fact that a single classifier cannot effectively classify and diagnose all diseases has been almost accorded with. This has seen a number of recent research attempts to arrive at a consensus using ensemble classification techniques. In this paper, a hybrid system is proposed to diagnose ailments using optimizing individual classifier parameters for two classifier techniques, namely, support vector machine (SVM) and multilayer perceptron (MLP) technique. We employ three recent evolutionary algorithms to optimize the parameters of the classifiers above, leading to six alternative hybrid disease diagnosis systems, also referred to as hybrid intelligent systems (HISs). Multiple objectives, namely, prediction accuracy, sensitivity, and specificity, have been considered to assess the efficacy of the proposed hybrid systems with existing ones. The proposed model is evaluated on 11 benchmark datasets, and the obtained results demonstrate that our proposed hybrid diagnosis systems perform better in terms of disease prediction accuracy, sensitivity, and specificity. Pertinent statistical tests were carried out to substantiate the efficacy of the obtained results.

Recent Advances in Computer-Aided Medical Diagnosis Using Machine Learning Algorithms With Optimization Techniques

“…Table 3 provides an outlook of recent advances of machine learning algorithms with optimizers in diabetes prediction. From the table we can see [34]- [44] have utilized several optimization techniques to enhance the overall prediction accuracy. Around all recent works have utilized Pima Indians Diabetes Database (PIDD).…”

“…Karegowda et al [34] integrated Genetic Algorithm and Black Propagation network (BPN) to implement a hybrid model. In the model they used Genetic Algorithm for optimizing and initializing the connection weights of Black Propagation network.…”

A Healthcare Monitoring System for the Diagnosis of Heart Disease in the IoMT Cloud Environment Using MSSO-ANFIS

“…The sensitivity of the proposed metaheuristic is high, and the accuracy achieved by this method is 94.03. Rao et al [30] proposed a hybrid metaheuristic technique using support vector machines and a multilayer perceptron for heart disease prediction. The proposed technique has application in e-Healthcare and telemedicine.…”

“…There are several studies [16,17,18,19,20,21,22] on machine learning that have been applied to heart disease prediction and other healthcare applications. However, there are fewer studies [25,29,30] which use hybrid metaheuristic techniques. A classification of algorithms used for optimization is shown in Fig.…”

“…Here, the performance of the proposed MSSO-ANFIS is analysed to determine its ability to identify the heart condition of patients. The proposed MSSO-ANFIS is compared with existing techniques, such as HOBDBNN [16], GA-RFNN [18], HRFLM [21], ANN-FuzzyAHP [22], 2 -DNN [24], logistics regression [25], ICA with metaheuristic [29] and hybrid intelligent systems [30] in terms of sensitivity, specificity, precision, F1-score, accuracy, and classification error. These measures are computed based on the standard formula.…”