INTEGRATED COMPUTATION EFFICIENT AND DYNAMIC MODELLING FOR HEART DISEASE PREDICTION USING OPTIMIZED INTELLIGENT LEARNING MODEL
In view of this paper, we aim for a deep learning that has been widely used to mine knowledgeable information from medical data bases. In Deep Learning classification is a supervised learning that can be used to design models describing important data classes where class attribute is involved in the construction of the classifier. Optimized Intelligent Learning Model is very intelligent highly efficient and effective algorithm for pattern recognition. Optimized Intelligent Learning Model is a computational and memory efficient classifier where samples are classified based on the class like human brain. Medical data bases are high volume in nature. If the data set contains redundant and irrelevant attributes classification may produce less accurate result. The proposed study is primarily concerned with improving feature selection and reducing the number of features yet giving better decisions. In this study to pick salient aspects of heart illness an improved optimization algorithm with a memory efficient approach is proposed. Experimental results shows that our algorithm enhance the accuracy in diagnosis of heart disease.
Performance Measures; Machine Learning; Heart Disease; Grid search