International Journal For Multidisciplinary Research

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Disease Prediction: Various Symptoms using Machine Learning

Author(s) Meghna Singh, Ashish Richhariya, Brijesh Gupta
Country India
Abstract In recent years, the use of machine learning algorithms has become popular in the healthcare industry for predicting diseases. In this paper, we propose a framework for disease prediction that utilizes three popular algorithms, Decision Tree, Random Forest Tree, and Naive Bayes. We have outlined disease prediction framework utilizing different Ml Calculations. The dataset utilized had more than 230 maladies for processing. Based on the side effects, age, sexual orientation of an individual, the conclusion framework gives the yield as the disease that the person may well be enduring from. The weighted Decision Tree calculation gave the finest comes about as compared to the other calculations. The exactness of the weighted Decision Tree calculation for the forecast was 95.17%. Other algorithms i.e. Random Forest Tree and Naive Bayes also gave the exactness of 95%. If a recommendation system can be made for doctors and medicine while using review mining will save a lot of time. In this type of system, the user face problem in understanding the heterogeneous medical vocabulary as the users are laymen. User is confused because a large amount of medical information on different mediums are available. The idea behind this system is to adapt with the special requirements of the health domain related with users.
Keywords Decision Tree, Random Forest Tree, Navie Bayes, Exactness
Field Computer Applications
Published In Volume 5, Issue 2, March-April 2023
Published On 2023-04-11
Cite This Disease Prediction: Various Symptoms using Machine Learning - Meghna Singh, Ashish Richhariya, Brijesh Gupta - IJFMR Volume 5, Issue 2, March-April 2023. DOI 10.36948/ijfmr.2023.v05i02.2329
DOI https://doi.org/10.36948/ijfmr.2023.v05i02.2329
Short DOI https://doi.org/gr5qwt
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