Abstract
Disease diagnosis is the identification of an health issue, disease, disorder, or other condition that a person may have. Disease diagnoses could be sometimes very easy tasks, while others may be a bit 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 which are used to diagnose a disease are manual and error-prone. Usage of Artificial Intelligence (AI) predictive techniques enables auto diagnosis and reduces detection errors compared to exclusive human expertise. In this paper, we have reviewed the current literature for the last 10 years, from January 2009 to December 2019. The study considered 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 most used AI techniques for medical diagnostic systems. We further discuss various diseases along with corresponding techniques of AI, including Fuzzy Logic, Machine Learning, and Deep Learning. This research paper aims to reveal some important insights into current and previous different 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.
Highlights
In the field of healthcare, the study of disease diagnosis plays a vital role
Research Goals To measure the accuracy of the framework system for heart disease The main goal of this research is to reduce time in processing of ECG signal by minimizing the number of data samples, without losing any essential bit and analysis the heart signals The prime objective of this study is to develop a fuzzy system to diagnose Ebola with useful recommendations To improve the segmentation process
SVM shows maximum in comparison to other three methods at 0.99 whereas the sensitivity of Logistic recorded at 0.94, Decision Tree recorded at 0.93 and KNN recoded at 0.96. 30 investigation on the Wisconsin Diagnostic Breast Cancer (WDBC) data set carried out in this method and recorded 97.38% accuracy which my be considered as better result
Summary
In the field of healthcare, the study of disease diagnosis plays a vital role. The causal study of disease is called the pathological process [1]. A disease is made by signs or symptoms that are interpreted by clinical experts [2]–[4]. Diagnosis has been defined as the method of identifying a disease from its signs and symptoms to conclude its pathology. Diagnosis of diseases is the most challenging process at the same time, a very pivotal phenomenon for a medical care professional as before reaching the conclusion
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