DIAGNOSIS OF ACUTE DISEASES USING AI

Disease diagnosis is the identification of a health issue, disease, disorder, or other condition that a person may have. Disease diagnoses could sometimes be very easy, while others may be trickier. There are large data sets available however, there is a limitation of tools that can accurately determine the patterns and make predictions. The traditional methods that are used to diagnose a disease are manual and error-prone. Using artificial intelligence (AI) predictive techniques enables automatic diagnosis and reduces detection errors compared to exclusive human expertise. This paper has reviewed the current literature for the last 10 years, from January 2009 to December 2019. The study considered the eight most frequently used databases, in which a total of 105 articles were found. A detailed analysis of those articles was conducted in order to classify the most used AI techniques for medical diagnostic systems. We further discuss various diseases along with corresponding techniques of AI, including Machine Learning, and Deep Learning. This research paper aims to reveal some important insights into current and previous AI techniques in the medical field used in today’s medical research, particularly in heart disease prediction, brain disease, prostate, liver disease, and kidney disease. Finally, the paper also provides some avenues for future research on AI-based diagnostics systems based on a set of open problems and challenges. So, in our project titled “Diagnosis of acute diseases using AI” is used to improve the diagnostic capabilities using AI. Here we have used four algorithms namely: Autoencoders, Collaborative Filtering, Reinforcement Learning, and Generative Adversarial Network. And at last, we have done a comparative analysis of all four algorithms to find which algorithm is more accurate. INDEX TERMS: AI Techniques, Deep Learning, Intelligent systems, Generative Adversarial Network ,autoencoders ,collaborative filtering, Reinforcement Learning