A Survey on Various Machine Learning Algorithms for Disease Prediction
Shubham Gupta1, Vishal Bharti2, Anil Kumar3
1Shubham Gupta, Department of CSE, DIT University, India.
2Vishal Bharti, Department of CSE, DIT University, India.
3Anil Kumar, Department of CSE, DIT University, India.
Manuscript received on 26 March 2019 | Revised Manuscript received on 03 April 2019 | Manuscript Published on 12 April 2019 | PP: 84-87 | Volume-7 Issue-6C April 2019 | Retrieval Number: F90350476C19/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The medical field regularly handles enormous amounts of data. Handling huge data by conventional methods can affect the results. Algorithms for machine learning can be used to find out facts in medical research, in particular for disease prediction. The early recognition of disease is crucial for the analysis of patient medicines and specialists. Machine learning algorithms like Decision trees, Support vector machine, Multilayer perceptron, Bayes classifiers, K-Nearest Neighbors Ensemble classifier techniques etc are used to determine various ailments. Using machine learning algorithms can lead to rapid disease prediction with high accuracy. This research paper analyzes how machine learning techniques are used to predict different diseases and its types. This paper examined research papers focusing mainly on the prediction of chronic kidney disease, machine learning, heart disease, diabetes, and breast cancer. The paper also examines the hybrid approach that increases the performance of individual classifiers.
Keywords: Artificial Neural Networks, K-Nearest Neighbor, Support Vector Machine, Principal Component Analysis.
Scope of the Article: Machine Learning