Abstract
The unavailability of sufficient information for proper diagnosis, incomplete or miscommunication between patient and the clinician, or among the healthcare professionals, delay or incorrect diagnosis, the fatigue of clinician, or even the high diagnostic complexity in limited time can lead to diagnostic errors. Diagnostic errors have adverse effects on the treatment of a patient. Unnecessary treatments increase the medical bills and deteriorate the health of a patient. Such diagnostic errors that harm the patient in various ways could be minimized using machine learning. Machine learning algorithms could be used to diagnose various diseases with high accuracy. The use of machine learning could assist the doctors in making decisions on time, and could also be used as a second opinion or supporting tool. This study aims to provide a comprehensive review of research articles published from the year 2015 to mid of the year 2020 that have used machine learning for diagnosis of various diseases. We present the various machine learning algorithms used over the years to diagnose various diseases. The results of this study show the distribution of machine learningmethods by medical disciplines. Based on our review, we present future research directions that could be used to conduct further research.
Highlights
The outcome of a treatment could be affected due to mistakes made by clinicians in the diagnosis of a patient [1]
This study suggested that early detection of glaucoma is possible using deep feed forward neural network (FNN) with the area under curve (AUC) value of 92.6%
machine learning (ML) is used in medical diagnosis for reduction in the overall cost of medical expenditure, and as a ‘second’ opinion for doctors
Summary
The outcome of a treatment could be affected due to mistakes made by clinicians in the diagnosis of a patient [1]. In a scenario of a diagnostic error, inappropriate treatment could be given where the patient would be deprived of the necessary care. Often the doctors are distracted by the features that seem important at the time, and make diagnostic errors [2]. The surrounding environment, and the tools used for diagnosis, can lead to diagnostic errors [3]. CMC, 2021, vol., no.2 mentioned factors could contribute to a significant adverse effect on the patient’s health, increase the overall medical expenditures, and cause psychological discomfort [4]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
