IJIRCST

Disease Prediction System using Support Vector Machine and Multilinear Regression

Md. Ehtisham Farooqui | Dr. Jameel Ahmad

Evolution of modern technologies like data science and machine learning has opened the path for healthcare communities and medical institutions, to detect the diseases earliest as possible and it helps to provide better patient care. Accuracy of detecting the possible diseases is reduced when we do not have complete medical data. Furthermore, certain diseases are region-based, which might cause weak disease prediction. Our body shows the symptoms when something wrong is happening within our body, sometime it may be just minor problem but sometimes we can have severe illness and if we do not take care of these symptoms at the early stage then it might be too late to cure the disease. So we are proposing a disease prediction system that can predict the possible diseases based on symptoms so it can be cured at the early stage. It saves time that is required to do the complete diagnosis of the patient and based on the suggestions provided by the system we can only get the patient diagnosed for those diseases that are required. In this paper, we are using machine learning algorithms that try to accurately predict possible diseases. The results generated by the proposed system have an accuracy of up to 87%. The system has incredible potential in anticipating the possible diseases more precisely. The main motive of this study is to help the nontechnical person and freshman doctors to make a correct opinion about the diseases.

Disease Prediction System, Machine Learning, Multilinear Regression (MLR), Support Vector Machine (SVM).

M. 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), Disease Prediction System using Support Vector Machine and Multilinear Regression, International Journal of Innovative Research in Computer Science & Technology (IJIRCST), Vol-8, Issue-4, Page No-331-336], (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)

DOI:

DOI: 10.21276/ijircst.2020.8.4.15 DOI URL: https://doi.org/10.21276/ijircst.2020.8.4.15

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)