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

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...

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Published in:Journal of Basic and Applied Research in Biomedicine Vol. 6; no. 2
Main Authors: Sagir Masanawa, Hamza Abubakar
Format: Journal Article
Language:English
Published: Journal of Basic and Applied Research in Biomedicine 25.01.2022
Subjects:
Fuzzy logic, Hybrid Learning Algorithm, Adaptive neuro fuzzy Inference system Feed forward Neural network
ISSN:2710-1479
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Abstract 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.
AbstractList 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.
Author Sagir Masanawa
Hamza Abubakar
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  organization: Department of Mathematical Sciences, Federal University Dutsin-Ma, Nigeria
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  fullname: Hamza Abubakar
  organization: Department of Mathematics, Isa Kaita College of Education, Dutsin-Ma, Katsina, Nigeria
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Snippet 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...
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SubjectTerms Fuzzy logic, Hybrid Learning Algorithm, Adaptive neuro fuzzy Inference system Feed forward Neural network
Title Sparse Matrix Approach in Neural Networks for Effective Medical Data Sets Classifications
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