Machine Learning Algorithms for Detection: A Survey and Classification

Abstract

There is an enormous amount of data being dealt with by the medical field on a daily basis. Using a conventional method for handling data can affect the accuracy of the results. Early recognition of the disease is crucial for the analysis of patient medicines and specialists. The objective of this paper is to provide a comprehensive review of the techniques used in disease detection. Machine learning algorithms can be used to find out facts in medical research, particularly disease prediction. Machine learning algorithms such as Support vector machine [SVM], Decision trees, Bayes classifiers, K-Nearest Neighbours [KNN] Ensemble classifier techniques, etc. are used to determine different ailments. The use of machine learning algorithms can lead to fast and high accuracy prediction of diseases. This research paper analyses how machine learning techniques and algorithms are used to predict different diseases and their types. This paper provides an extensive survey of the machine learning techniques used for the prediction of chronic kidney disease, liver disease, haematological diseases, Alzheimer’s disease, and urinary tract infections.