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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM20157

DISEASE PREDICTION AND DRUG RECOMMENDATION SYSTEM USING MACHINE LEARNING

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM20157

Volume:

7 |

4 |

Apr 2023

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

N

Nikitha.V1

L

L.Pallavi2

KK

K.Sharan Kumar3

MR

M.Akhay Raj4

GK

G.Prasanna Kumar5

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Abstract

Medical decisions could be extremely specialized and difficult jobs due to alternative factors or in case of rare diseases. This problem is complex due to the large amount of data required to make accurate predictions, the complexity of disease development, and the variability between patients is difficult. And it is a very time consuming process. Additionally, there is a need to ensure that patient data is protected and used ethically, while also providing actionable insights for healthcare providers. Our proposed system is developed using python. This website is developed to help the doctors, patients to identify the disease. To reduce the large number of variables and find the most probable diseases by using the Random Forest, decision trees, SVM algorithms. And this system recommends the drug based on the factors we provide such as age, gender, bp etc. It is not only useful for doctors but also for the people who want to predict their disease.

KEYWORDS

disease prediction SVM Random Forest drug recomendation

Cite This Article

Nikitha.V1, L.Pallavi2, K.Sharan Kumar3, M.Akhay Raj4, G.Prasanna Kumar5, "DISEASE PREDICTION AND DRUG RECOMMENDATION SYSTEM USING MACHINE LEARNING," International Journal of Scientific Research in Engineering and Management, vol. 7, no. 4, Apr 2023. https://doi.org/10.55041/IJSREM20157

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Publication Date

3/4/2023

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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