Enhanced Sparse Matrix Approach in Neural Network Algorithm for an Effective Intelligent Classification System

The main objective of this research is to develop an effective intelligent system that can be used by medical practitioners (physicians) to accelerate diagnosis and treatment processes. In this paper, the sparse matrix approach was incorporated in neural network learning algorithm for scalability, minimize higher memory usage/storage capacity, enhancing implementation time and speed up the analysis of the data. The proposed intelligent classification system maximizes the intelligently classification of data and minimizes the number of trends inaccurately identified. For robustness, the proposed method was tested with three different datasets, namely, Hepatitis, SPECT Heart and Cleveland Heart. Therefore, an attempt was made to determine the performance indicators efficacy. Compared to some similar existing methods, the approach presented achieves improved performance. The program used for implementation of the proposed model is MATLAB R2016a (version 9.0) and executed in the 4.0 GB RAM processor of PC Intel Pentium Quad Core N3700.