Hybrid Real-coded Genetic Algorithm and MIMO CMAC NN Classifier for Solving Medical Data Classification Problems

2013 ◽  
pp. 46-56
Author(s):  
Jui-Yu Wu
Keyword(s):  
Genetic Algorithm ◽  
Data Classification ◽  
Medical Data ◽  
Classification Problems ◽  
Real Coded Genetic Algorithm ◽  
Medical Data Classification

Sparse Matrix Approach in Neural Networks for Effective Medical Data Sets Classifications

2020 ◽  
Vol 6 (2) ◽  
pp. 90-97
Author(s):  
Sagir Masanawa ◽  
Hamza Abubakar
Keyword(s):  
Intelligent System ◽  
Sparse Matrix ◽  
Data Classification ◽  
Medical Data ◽  
Data Sets ◽  
Matrix Approach ◽  
Neural Network Learning ◽  
Network Learning ◽  
Hybrid Intelligent System ◽  
Medical Data Classification

In this paper, a hybrid intelligent system that consists of the sparse matrix approach incorporated in neural network learning model as a decision support tool for medical data classification is presented. The main objective of this research is to develop an effective intelligent system that can be used by medical practitioners to accelerate diagnosis and treatment processes. The sparse matrix approach incorporated in neural network learning algorithm for scalability, minimize higher memory storage capacity usage, enhancing implementation time and speed up the analysis of the medical data classification problem. The hybrid intelligent system aims to exploit the advantages of the constituent models and, at the same time, alleviate their limitations. The proposed intelligent classification system maximizes the intelligently classification of medical data and minimizes the number of trends inaccurately identified. To evaluate the effectiveness of the hybrid intelligent system, three benchmark medical data sets, viz., Hepatitis, SPECT Heart and Cleveland Heart from the UCI Repository of Machine Learning, are used for evaluation. A number of useful performance metrics in medical applications which include accuracy, sensitivity, specificity. The results were analyzed and compared with those from other methods published in the literature. The experimental outcomes positively demonstrate that the hybrid intelligent system was effective in undertaking medical data classification tasks.

Hybrid mechanism for medical data classification

2021 ◽  
Vol 19 (3/4) ◽  
pp. 243
Author(s):  
Ahelam Mainoddin Tikotikar ◽  
Mallikarjun M. Kodabagi
Keyword(s):  
Data Classification ◽  
Medical Data ◽  
Hybrid Mechanism ◽  
Medical Data Classification
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