Disease Prediction System

Rushikesh, Khiratkar and Shiya, Sarpate and Gaurav, Dhande and Bhavna, Meshram and Prof. Rina, Shirpurkar (2024) Disease Prediction System. International Journal of Trend in Scientific Research and Development, 8 (5). pp. 1023-1031. ISSN 2456-6470

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Abstract

The proposed disease prediction system utilizes machine learning algorithms and data analytics to predict the likelihood of diseases based on individual health profiles. The system integrates electronic health records (EHRs), genomic data, and environmental factors to provide personalized risk assessments. Artificial images do not need to try to represent any real object, person, or place. For this purpose, techniques that perform a pixel-level feature extraction are used. The first one is Photo Response Non-Uniformity (PRNU). PRNU is a special noise due to imperfections on the camera sensor that is used for source camera identification. The underlying idea is that AI images will have a different PRNU pattern. The second one is error level analysis (ELA). This is another type of feature extraction traditionally used for detecting image editing. The rise of chronic diseases poses significant challenges to healthcare systems worldwide, necessitating innovative solutions for early diagnosis and prevention. This project presents a comprehensive disease prediction system designed to harness the power of data analytics and machine learning to identify individuals at risk of developing various health conditions. By integrating diverse data sources, including electronic health records (EHRs), demographic information, lifestyle factors, and genetic data, our system aims to provide accurate and timely predictions that can facilitate proactive healthcare management. Moreover, we recognize the importance of real-time data integration in contemporary healthcare. As part of our future enhancements, we plan to incorporate wearable health technology and mobile health applications to continuously monitor patient health metrics. This integration will enable dynamic updates to risk assessments and ensure that the predictive model evolves alongside emerging health trends The architecture of the proposed system consists of several key components. First, we implemented robust data preprocessing techniques to clean and normalize the datasets, ensuring that the input data is of high quality. Next, we utilized a range of machine learning algorithms, including decision trees, support vector machines, and ensemble methods, to develop predictive models tailored to specific diseases such as diabetes, cardiovascular diseases, and hypertension. Through rigorous training and validation processes, we achieved high levels of accuracy, precision, and recall in our predictions, demonstrating the efficacy of our approach.

Item Type:	Article
Subjects:	Q Science > Q Science (General)
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	26 Oct 2024 09:19
Last Modified:	26 Oct 2024 09:19
URI:	http://eprints.umsida.ac.id/id/eprint/14428

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