HeathDetect7: Multiple Disease Identification Using Machine Learning

A. Chandana¹ , S. Sai Ashrith² , R. Jayanth³ , M. Prashanth⁴

Section:Research Paper, Product Type: Journal
Vol.12 , Issue.2 , pp.19-26, Apr-2024

Online published on Apr 30, 2024

Copyright © A. Chandana, S. Sai Ashrith, R. Jayanth, M. Prashanth . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Numerous machine learning models in healthcare focus on single disease detection, yet there`s a growing need for systems that predict multiple diseases using a unified interface. This research addresses this gap by leveraging machine learning techniques to analyse diverse medical datasets and provide personalized risk assessments for diseases such as COVID-19, brain tumours, breast cancer, heart disease, diabetes, Alzheimer`s, and pneumonia. These diseases are causing many deaths globally, often due to the lack of timely check-ups and medical interventions. This problem is intensified by inadequate medical infrastructure and a low ratio of doctors to the population. By incorporating medical imaging data and clinical parameters, this study offers a comprehensive approach to disease identification, enabling early intervention and improved health outcomes. The project`s user-friendly interface allows individuals to input their medical information easily and receive timely assessments. Various classification algorithms, such as Random Forest, eXtreme Gradient Boosting (XGBoost), Convolutional Neural Networks (CNN), and Visual Geometry Group-16 (VGG-16), are explored to achieve accurate disease prediction. The ultimate goal is to create a web application that leverages machine learning to forecast several diseases, contributing to proactive healthcare management, and empowering individuals to monitor their health proactively and make informed decisions about their well-being.

Key-Words / Index Term :
Unified interface, Personalized risk assessments, Clinical parameters, User-friendly interface, Proactive healthcare management, Informed decisions, Medical imaging data, Random Forest, XGBoost, CNN, VGG-16.

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