IJIRCST

A Detailed Review on Disease Prediction Models that uses Machine Learning

Md. Ehtisham Farooqui | Dr. Jameel Ahmad

Human body is guarded by the immune system, but sometimes this immune system alone is not capable of preventing our body from diseases. Environmental conditions and living habits of people are the cause of many diseases that are the main reason for a huge number of deaths in the world, and diagnosing these diseases sometimes becomes challenging. We need an accurate, feasible, reliable, and robust system to diagnose diseases in time so that these can be properly treated. With the growth of medical data, many researchers are using these medical data and some machine learning algorithms to help the healthcare communities in the diagnosis of many diseases. In this paper a survey of various models based on such algorithms, techniques is presented and their performance is analyzed. Researches have been conducted on various models of supervised learning algorithms and some of them are Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree (DT), Naïve Bayes and Random Forest (RF).

Decision Tree, Machine Learning, Naïve Bayes, Random Forest

. Chen, Y. Hao, K. Hwang, L. Wang, and L. Wang, “Disease prediction by machine learning over big data from healthcare communities” IEEE Access, vol. 5, no. 1, pp. 8869–8879, 2017.
Sayali Ambekar, Rashmi Phalnikar, “Disease Risk Prediction by Using Convolutional Neural Network” IEEE, 978-1-5386-5257-2/18, 2018.
Naganna Chetty, Kunwar Singh Vaisla and Nagamma Patil, “An Improved Method for Disease Prediction using Fuzzy Approach” IEEE, DOI 10.1109/ICACCE.2015.67, pp. 569-572, 2015.
Dhiraj Dahiwade, Gajanan Patle and Ektaa Meshram, “Designing Disease Prediction Model Using Machine Learning Approach” IEEE Xplore Part Number: CFP19K25-ART; ISBN: 978-1-5386-7808-4, pp. 1211-1215, 2019.
Lambodar Jena and Ramakrushna Swain, “Chronic Disease Risk Prediction using Distributed Machine Learning Classifiers” IEEE, 978-1-5386-2924-6/17, pp. 170-173, 2017.
Dhomse Kanchan B. and Mahale Kishor M., “Study of Machine Learning Algorithms for Special Disease Prediction using Principal of Component Analysis” IEEE, 978-1-5090-0467-6/16, pp. 5-10, 2016.
Pahulpreet Singh Kohli and Shriya Arora, “Application of Machine Learning in Disease Prediction” IEEE, 978-1-5386-6947-1/18, pp. 1-4, 2018.
Deeraj Shetty, Kishor Rit, Sohail Shaikh and Nikita Patil, ” Diabetes Disease Prediction Using Data Mining” IEEE, 978-1-5090-3294-5/17, 2017.
Rashmi G Saboji and Prem Kumar Ramesh,“A Scalable Solution for Heart Disease Prediction using Classification Mining Technique” IEEE, 978-1-5386-1887-5/17, pp. 1780-1785, 2017.
Rati Shukla, Vikash Yadav, Parashu Ram Pal and Pankaj Pathak, "Machine Learning Techniques for Detecting and Predicting Breast Cancer" IJITEE, ISSN: 2278-3075, Volume-8, pp. 2658-2662, 2019.
Senthilkumar Mohan, Chandrasegar Thirumalai and Gautam Srivastava, “Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques” IEEE Access, DOI 10.1109/ACCESS.2019.2923707, pp. 81542-81554, 2019.
Anjan Nikhil Repaka, Sai Deepak Ravikanti and Ramya G Franklin,”Design And Implementing Heart Disease Prediction Using Naives Bayesian” IEEE Xplore Part Number: CFP19J32-ART; ISBN: 978-1-5386-9439-8, pp. 292-297, 2019.
Aakash Chauhan, Purushottam Sharma, Vikas Deep and Aditya Jain, “Heart Disease Prediction using Evolutionary Rule Learning” CICT 2018.
Aditi Gavhane, Gouthami Kokkula, Isha Pandya and Kailas Devadkar, “Prediction of Heart Disease Using Machine Learning” IEEE Xplore ISBN: 978-1-5386-0965-1, pp. 1275-1278, 2018.
Ankita Dewan and Meghna Sharma, “Prediction of Heart Disease Using a Hybrid Technique in Data Mining Classification” IEEE, 978-9-3805-4416-8/15, pp. 704-706, 2015.

[Md. Ehtisham Farooqui , Dr. Jameel Ahmad (2020) A Detailed Review on Disease Prediction Models that uses Machine Learning IJIRCST Vol-8 Issue-4 Page No-326-330] (ISSN 2347 - 5552). www.ijircst.org

Md. Ehtisham Farooqui

Student, Department of Computer Science and Engineering, Integral University, Lucknow, India, (e-mail: mefxe01@gmail.com)