Issue |
ITM Web Conf.
Volume 65, 2024
International Conference on Multidisciplinary Approach in Engineering, Technology and Management for Sustainable Development: A Roadmap for Viksit Bharat @ 2047 (ICMAETM-24)
|
|
---|---|---|
Article Number | 03002 | |
Number of page(s) | 14 | |
Section | Computer Engineering and Information Technology | |
DOI | https://doi.org/10.1051/itmconf/20246503002 | |
Published online | 16 July 2024 |
CureIt – A Multidisease Predictive System using Machine Learning
1,2 M.E. Student, Department of Computer Engineering, GTU, Ahmedabad, India
3 Faculty, Department of Computer Engineering, SPU, Visnagar, India
* apoorva.cse1@gmail.com
ravaljinit@gmail.com
rrchaudhari.ce@spcevng.ac.in
Many contemporary machine learning approaches perform well in predictive analytics on large datasets from many sectors. While predictive analytics in healthcare remains an ambitious goal, it has the potential to enable healthcare practitioners to make more educated decisions regarding patient health and treatment. Globally, mortality from illnesses such as diabetes, heart disease, and breast cancer is frequently caused by a lack of regular checks, even when early indications are present. Limited access to doctors and limited medical infrastructure worsen the problem. The WHO suggests a doctor-to-patient ratio of 1:1000 [8], while India’s ratio of 1:8342 [20] has improved, but distant healthcare access remains a concern. Early diagnosis of heart, cancer, and diabetes-related illnesses has a huge influence on public health. This study aims to use ML algorithms to anticipate illnesses in their early stages. Our team created an online medical test application that uses machine learning to anticipate diseases, with the goal of making healthcare more accessible. Our goal is to develop a web and mobile app that anticipates diseases and provides medical advice, such as diabetes, heart disease, and cancers.
Key words: Deep Learning / TensorFlow / SVM / Logistic Regression / Parkinson’s / Diabetes / Tumor / RF / ANN
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.