Multi-objective differential evolution based random forest for e-health applications

Abstract

Many machine learning techniques have been used in past few decades for various medical applications. However, these techniques suffer from parameter tuning issue. Therefore, an efficient tuning of these parameters has an ability to improve the performance of existing machine learning techniques. Therefore, in this work, a novel multi-objective differential evolution based random forest technique is proposed. The proposed technique is able to tune the parameters of random forest in an efficient manner. Extensive experiments are carried out by considering the proposed and the existing competitive machine learning techniques on various medical applications. It is observed that the proposed technique outperforms existing techniques in terms of accuracy, f-measure, sensitivity and specificity.

Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks

“…Specificity (S p ) computes the proportion of actual negatives that are correctly identified and it can be estimated as [37]:…”

Efficient prediction of drug–drug interaction using deep learning models

“…For comparative analysis, nine well‐known predictions models are considered. These models are decision tree (DT) [30], random forest (RF) [31], L1 norm support vector machine (L1‐SVM) [32], L2 norm support vector machine (L2‐SVM) [33], artificial neural networks (ANNs) [34], k‐nearest neighbour (kNN) [35], CNN [36], long short term memory (LSTM) networks [37] and adaptive neuro‐fuzzy inference system (ANFIS) [38]. The parameters setting of the proposed and the competitive models are their default values as mentioned in their respective literature.…”

Towards the Control and Prevention of Waste in IT Service Operation Using Fuzzy Logic: Focus in Incident Management Process

“…In addition, this approach supports several Lean approaches as much as to improve the management processes in operation and remove waste according to Fuzzy-AHP method [27] [28], but it differs from the fact that it adapts specific altitudes of IT services treatments, namely the intangibility, subjectivity and user perception. This way opens perspectives towards other techniques using machine learning [33] or Fuzzy c-means (FCM) [29].…”