CFLCA: High Performance based Heart disease Prediction System using Fuzzy Learning with Neural Networks

Human Diseases are increasing rapidly in today’s generation mainly due to the life style of people like poor diet, lack exercises, drugs and alcohol consumption etc. But most spreading disease that is commonly around 80% death direct indirectly heart basis. In future (approximately after 10 years) maximum number may expire cause diseases. Due these reasons, many researchers providing enormous remedy, data analysis various proposed technologies for diagnosing diseases with plenty medical which related disease. field Medicine regularly receives very wide range form text, image, audio, video, signal pockets, This database contains raw dataset consist inconsistent redundant data. The health care system no doubt rich aspect storing but at same time fetching knowledge. Data mining (DM) methods can help extracting a valuable knowledge by applying DM terminologies clustering, regression, segmentation, classification After collection when becomes larger more complex than algorithms clustering (D-Tree, Neural Networks, K-means, etc.) used. To get accuracy precision values improved method Cognitive Fuzzy Learning based Clustering Algorithm (CFLCA) method. CFLCA methodology creates advanced meta indexing n-dimensional unstructured used enrichment feature engineering UCI machine learning algorithm, attain high level accurate prediction rate. Through this algorithm having accuracy, recall detection.