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Predicting Co-occurring Diseases with Machine Learning: A Multi-Label Classification Approach

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Predicting Co-occurring Diseases with Machine Learning: A Multi-Label Classification Approach


Shreya Dadwe



Shreya Dadwe "Predicting Co-occurring Diseases with Machine Learning: A Multi-Label Classification Approach" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Advancements and Emerging Trends in Computer Applications - Innovations, Challenges, and Future Prospects, March 2025, pp.1443-1449, URL: https://www.ijtsrd.com/papers/ijtsrd80017.pdf

The sheer volume of health information we're generating today, and the fact that so many widespread and infectious diseases are out there, has really made it critical that we have smart tools that can help us figure out what's going on with our health. You know, the traditional ways of diagnosing diseases can take an eternity, cost a lot of money, and even lead to mistakes, particularly when doctors are trying to sort through a lot of possibilities at the same time. So, this research is all about creating a machine learning model – a smart assistant, basically – that can review a patient's basic health information and history and predict how likely they are to get a variety of diseases. The aim is to give healthcare professionals an early warning so that they can head things off and prepare the best response. We implemented a few of the various ways that these smart assistants learn in this project. We trained models using techniques such as Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines (or SVM for short) to forecast things like diabetes, heart conditions, and Parkinson's. To make sure these models were working well, we cleaned the data used first. This meant choosing the most important information, making sure everything was working on the same level, and killing in the gaps. We then trained the models with measurements such as accuracy, precision, recall, and the F1-score to see which learned best. We also contrasted how each model performed differently for different illnesses.

Machine learning, Multi-Disease Prediction, Healthcare data, clinical data.


IJTSRD80017
Special Issue | Advancements and Emerging Trends in Computer Applications - Innovations, Challenges, and Future Prospects, March 2025
1443-1449
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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