Open AccessGeneric Disease Prediction using Symptoms with Supervised Machine Learning
Author(s) -
Ashish Pal,
Pritam Rawal,
Rahil Ruwala,
Vaibhavi Patel
Publication year - 2019
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit1952297
Subject(s) - naive bayes classifier , random forest , machine learning , artificial intelligence , field (mathematics) , computer science , bayes' theorem , disease , domain (mathematical analysis) , health care , test (biology) , data mining , data science , support vector machine , medicine , mathematics , bayesian probability , pathology , mathematical analysis , paleontology , pure mathematics , economics , biology , economic growth
Data Mining and Machine Learning plays most inspiring area of research that become most popular in health organization. It also plays a vital part to uncover new patterns in medicinal science and services association which thusly accommodating for all the parties associated with this field. This project intend to form a diagnostic model of the common diseases based on the symptoms by using data mining technique such as classification in health domain. In this project, we are going to use algorithms like Random forest, Naive Bayes which can be utilized for health care diagnosis. Performances of the classifiers are compared to each other to find out highest accuracy. This also helps us to find out persons who are affected by the infection. The test based on the outcomes of the diseases.