Symptom Based Disease Prediction Using Machine Learning
Ridham Sood1, Virat Sharma2
1Ridham Sood, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
2Virat Sharma, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.
Manuscript received on 25 July 2024 | Revised Manuscript received on 13 August 2024 | Manuscript Accepted on 15 September 2024 | Manuscript published on 30 September 2024 | PP: 7-10 | Volume-4 Issue-6, September 2024 | Retrieval Number: 100.1/ijpmh.G92340811922 | DOI: 10.54105/ijpmh.G9234.04060924
Open Access | Ethics and Policies | Cite | Zenodo | OJS | Indexing and Abstracting
© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The Disease Prediction Method uses predictive modeling to predict the user’s disease based on the symptoms that the user offers as feedback to the system. Medical services are in desperate need to be advanced in order to make better choices about patient care and treatment options. In terms of machine learning, Healthcare enables humans to process large and complex medical databases, interpret them, and derive clinical insights. The machine analyzes the user’s symptoms asinput and returns the disease’s likelihood as an output. Implementing the Decision Tree, K Nearest Neighbor, Naïve Bayes and Random Forest allows for disease prediction. In this paper, we attempt to integrate machine learning capabilities in healthcare into a single framework. Instead of diagnosis, healthcare can be made smart by implementing disease prediction using machine learning predictive algorithms. When an early diagnosis of a disease is not possible, certain cases may arise. As a result, disease prediction can be applied effectively. This paper focuses primarily on the creation of a scheme, or what we would call an immediate medical provision, that would integrate symptoms obtained from multisensory devices as well as other medical data and store it in a healthcare dataset. This Dataset would be analyze using machine learning algorithm with accuracy more than 90%.
Keywords: Machine Learning Disease Prediction, Decision K Nearest Neighbor, Naïve Bayes, Random Forest.
Scope of the Article: Health Care Management