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Advancing Horizons in Chronic Diseases: Research Innovation Insights

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Advancing Horizons in Chronic Diseases: Research Innovation Insights


Vaishnavi Watkar | Shivani Ayyagari | Prof. Anupam Chaube



Vaishnavi Watkar | Shivani Ayyagari | Prof. Anupam Chaube "Advancing Horizons in Chronic Diseases: Research Innovation Insights" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Emerging Trends and Innovations in Web-Based Applications and Technologies, January 2025, pp.141-146, URL: https://www.ijtsrd.com/papers/ijtsrd74929.pdf

Technological development, including machine learning, has a huge impact on health through an effective analysis of various chronic diseases for more accurate diagnosis and successful treatment. In the field of biomedical and healthcare communities the accurate prediction plays the major role to find out the risk of the disease in the patient. The only way to overcome with the mortality due to chronic diseases is to predict it earlier so that the disease prevention can be done. Such model is a Patient’s need in which Machine Learning is highly recommendable. But the precise prediction on the basis of symptoms becomes too difficult for doctor. The correct prediction of disease is the most stretching task. To overcome this problem data mining plays an important role to predict the disease. We use Heart disease, Kidney disease, Cancer disease and Diabetes disease datasets, In order to build reliable prediction models for these chronic diseases using data mining techniques. The most relevant features are selected from the dataset for improved accuracy and reduced training time. The system analyzes the symptoms provided by the user as input and gives the probability of the disease as an output Disease by using, random forest and decision tree we are predicting diseases like Diabetes, Heart, Cancer and Kidney. For each chronic disease, diverse models, techniques, and algorithms are used for predicting and analyzing. The common prediction objective is to minimize the prediction error as low as possible. The final discussions of this paper are works in improving the prediction performance for chronic diseases using a data preprocessing handling.

Chronic Diseases, Machine Learning, Diseases Prediction, Accuracy, Prediction performance


IJTSRD74929
Special Issue | Emerging Trends and Innovations in Web-Based Applications and Technologies, January 2025
141-146
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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