Medical Diagnosis Assistance

Medical diagnosis assistance is a field that uses technology, such as artificial intelligence and machine learning, to help healthcare professionals diagnose diseases and conditions.It involves analyzing patient data, including symptoms, medical history, and test results, to provide potential diagnoses or recommendations.This project aims to revolutionize healthcare by leveraging patient symptoms to detect infections or diseases accurately and swiftly.By utilizing advanced technology and machine learning algorithms, this approach enhances early diagnosis, leading to timely medical intervention and improved patient outcomes.Specifically, it involves leveraging a Random Forest Classifier, a powerful machine learning algorithm, to predict diseases based on symptomatic data.The goal is to expedite and enhance the diagnostic process, providing a valuable tool for healthcare professionals.

Authors

Publication Details

Journal
International Journal of Research Publication and Reviews
Published
2025-05-01
DOI
https://doi.org/10.55248/gengpi.6.0525.1935
Primary Topic
Clinical Reasoning and Diagnostic Skills
Type
article
Field-Weighted Citation Impact
0.00
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article

Medical Diagnosis Assistance

Puli Chandra Obul Reddy, Premkumar Chithaluru, R. B. REDDY, Bharath Manchana
International Journal of Research Publication and Reviews
Clinical Reasoning and Diagnostic Skills
article

Medical Diagnosis Assistance

Puli Chandra Obul Reddy, Premkumar Chithaluru, R. B. REDDY, Bharath Manchana
article en

Abstract

Medical diagnosis assistance is a field that uses technology, such as artificial intelligence and machine learning, to help healthcare professionals diagnose diseases and conditions.It involves analyzing patient data, including symptoms, medical history, and test results, to provide potential diagnoses or recommendations.This project aims to revolutionize healthcare by leveraging patient symptoms to detect infections or diseases accurately and swiftly.By utilizing advanced technology and machine learning algorithms, this approach enhances early diagnosis, leading to timely medical intervention and improved patient outcomes.Specifically, it involves leveraging a Random Forest Classifier, a powerful machine learning algorithm, to predict diseases based on symptomatic data.The goal is to expedite and enhance the diagnostic process, providing a valuable tool for healthcare professionals.

International Journal of Research Publication and ReviewsVol. 6(5)
Openalex Percentile: Top 86%
Clinical Reasoning and Diagnostic Skills
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