Reinforcing Artificial Neural Networks through Traditional Machine Learning Algorithms for Robust Classification of Cancer

作     者：Muhammad Hammad Waseem Malik Sajjad Ahmed Nadeem Ishtiaq Rasool Khan Seong-O-Shim Wajid Aziz Usman Habib 

作者机构：Department of Computer Sciences and Information TechnologyUniversity of Azad Jammu and KashmirMuzaffarabad13100Pakistan Department of Computer Science and AICollege of Computer Science and EngineeringUniversity of JeddahJeddah23890Saudi Arabia Department of Computer and Network EngineeringCollege of Computer Science and EngineeringUniversity of JeddahJeddah23890Saudi Arabia Department of AI&DSSchool of ComputingNational University of Computer and Emerging SciencesIslamabad44000Pakistan 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第75卷第5期

页      面：4293-4315页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The authors thank Aliya Shaheen  Assistant Professor (Mathematics)  for her support and guidance during this study 

主　　题：ANN decision support systems gene-expression data hybrid classification machine learning predictive analytics 

摘      要：Machine Learning(ML)-based prediction and classification systems employ data and learning algorithms to forecast target ***,improving predictive accuracy is a crucial step for informed *** the healthcare domain,data are available in the form of genetic profiles and clinical characteristics to build prediction models for complex tasks like cancer detection or *** ML algorithms,Artificial Neural Networks(ANNs)are considered the most suitable framework for many classification *** network weights and the activation functions are the two crucial elements in the learning process of an *** weights affect the prediction ability and the convergence efficiency of the *** traditional settings,ANNs assign random weights to the *** research aims to develop a learning system for reliable cancer prediction by initializing more realistic weights computed using a supervised setting instead of random *** proposed learning system uses hybrid and traditional machine learning techniques such as Support Vector Machine(SVM),Linear Discriminant Analysis(LDA),Random Forest(RF),k-Nearest Neighbour(kNN),and ANN to achieve better accuracy in colon and breast cancer *** system computes the confusion matrix-based metrics for traditional and proposed *** proposed framework attains the highest accuracy of 89.24 percent using the colon cancer dataset and 72.20 percent using the breast cancer dataset,which outperforms the other *** results show that the proposed learning system has higher predictive accuracies than conventional classifiers for each dataset,overcoming previous research ***,the proposed framework is of use to predict and classify cancer patients ***,this will facilitate the effective management of cancer patients.