Multiple Disease Prediction System: A Review

Authors

  • Aayushi Agrawal Student, Department of Information Technology, SVKM's NMIMS Mukesh Patel School of Technology Management and Engineering, Shirpur, India
  • Shubham Dalal Student, Department of Information Technology, SVKM's NMIMS Mukesh Patel School of Technology Management and Engineering, Shirpur, India
  • Dhvanan Rangrej Student, Department of Information Technology, SVKM's NMIMS Mukesh Patel School of Technology Management and Engineering, Shirpur, India
  • Nitin Choubey Head of the Department of Information Technology, SVKM's NMIMS Mukesh Patel School of Technology Management and Engineering, Shirpur, India

Keywords:

Support Vector Machine (SVM), Diabetes, Naive Bayes, Heart Disease, Breast Cancer, Convolutional neural network (CNN), Data Mining, K Nearest Neighbor (KNN), Decision Tree

Abstract

Machine Learning techniques are used for a lot of applications. In healthcare, machine learning plays a critical role in disease prediction. For detecting a disease, several tests must be required from the patient. By using machine learning techniques, the number of tests can be reduced. This reduced test performs a critical function in time and performance. This article analyzes machine learning strategies that can be used to predict distinct varieties of diseases. This paper reviewed the research papers which especially deal with predicting Diabetes, Heart disease, and Breast cancer. This article presents a review of various models based on such algorithms, techniques, and an analysis of their performance. Research has been carried out on various models of supervised learning algorithms and some of them are Support Vector Machine (SVM), K Nearest Neighbor (KNN), Decision Tree, Naïve Bayes, Convolutional Neural Network and Random Forest (RF).

 

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Published

31-10-2021

How to Cite

[1]
A. Agrawal, S. Dalal, D. Rangrej, and N. Choubey, “Multiple Disease Prediction System: A Review”, IJRAMT, vol. 2, no. 10, pp. 148–153, Oct. 2021.

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Articles