DETECTION OF HEART DISEASE BY USING RELIABLE BOOLEAN MACHINE LEARNING ALGORITHM

Artificial Intelligence (A.I) is one of most exciting fields of computer engineering today. It is the science and technique used to make machine intelligent and it is vast and truly universal field. However, tremendous growth has been observed in this filed in past two decade owing to valuable contributions from variety of domains. It has numerous potential applications such as computer vision, medicine, philosophy, psychology, linguistics, automatic programming, natural language processing, speech processing and robotics, etc. Machine Learning takes training from natural events and helps in predicting any type of event and is a branch of Artificial Intelligence (AI). Over the past two decades, Machine Learning became a major source for information technology in developing applications, such as manufacturing industry for automation in assembly line, biometric recognition, handwriting recognition, medical diagnosis, speech recognition, text retrieval, natural language processing and Machine Learning is widely using in Data Science (DS), it is predominant and hotcake field of 21st century. Today all of use machine learning several times a day, without knowing it. Examples of such "ubiquitous" or "invisible" usage include search engines, customer-adaptive web services, email managers (spam filters), computer network security, and so on. Since last few decades Cardiovascular(Heart) Diseases (CVDs) has emerged as the most life-threatening diseases and proved to be fatal not only in India but throughout the whole world. In time detection, diagnosis and treatment of the disease needs a reliable, accurate and feasible system. In this paper we proposed Reliable Boolean Machine Learning Algorithm (RBMLA) by using novel approach to predict heat disease. Finally performance of RBMLA is measured by using various performance metrics like accuracy, precision, recall, sensitivity, specificity, reliability, F-score and ROC curve. It is shown that it gives better performance for given any new test data and new real time data. It has given better accuracy of 86%.