Main Article Content
Abstract
Heart diseases are the major problem in the world now a days. Majority if the people are suffering from heart disease. Even number of deaths is also increasing due to heart diseases. So, it is very important to diagnose heart diseases and treat them in early stages. This problem is more in undeveloped areas, where number of doctors is very less and people do not have much awareness. They often come to know about the heart diseases in the last stages and difficulty arise to treat them in last stages. So, with the help of artificial intelligence, researchers tried to solve this serious problem. They had applied different algorithms for detection of heart diseases. In simple language, it can be said that they had developed medical diagnostic systems for diagnosis of heart diseases. Numerous researchers have done a lot work in this direction and they are able to get good results. Similarly, work had been done to diagnose other diseases as well, but here in this paper the focus is only on diagnosis of heart diseases.In this paper, the contribution made by different authors for diagnosis of heart diseases is stated. Some medical diagnostic systems developed by different researchers for diagnosis of heart diseases are described here in this study. Most of the authors have used the concepts of machine learning, deep learning and soft computing for diagnosis of heart diseases. Researchers have diagnosed heart diseases using simple rule-based approach, fuzzy logic, neuro fuzzy algorithms, artificial neural network, supervised machine learning algorithms, unsupervised machine learning algorithms, Deep learning techniques like CNN etc. Different tools and programming languages such as MatLab, python etc. are used to implement these systems and performance of these systems are calculated in terms of different performance parameters such as accuracy, recall, sensitivity, specificity, precision etc.