Kidney Disease Prediction

Registration ID: IJNRD_215992

Published ID: IJNRD2403472

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Boddeda Ajay, Aravapalli Jabali Rushi, Kantamneni Abhinay Siddhartha

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Kidney disease remains a pressing global health concern, with early detection proving essential for effective management and treatment. This project introduces an innovative approach to kidney disease prediction through the integration of machine learning techniques into a user-friendly web application. Our system seeks to empower both healthcare professionals and patients by providing a convenient and accessible tool for assessing kidney disease risk based on blood work data. At the core of our project lies a fusion of tailored machine learning algorithms with web development technologies, including Flask, HTML, and CSS. Through a meticulously designed web interface, users can input relevant blood work parameters of patients, enabling the machine learning model to generate predictions regarding the likelihood of kidney disease occurrence. Key features of our system include an intuitive user interface, robust data validation mechanisms, and error handling functionalities to ensure a seamless user experience. By integrating performance evaluation metrics, such as accuracy, precision, recall, and F1 score, users gain valuable insights into the reliability and accuracy of the prediction model. Additionally, efforts have been made to enhance prediction interpretability, aiding users in comprehending the underlying factors contributing to the risk assessment. Security measures have been meticulously implemented to safeguard sensitive medical data, including encryption and authentication protocols. Scalability has been a primary consideration in the system’s design, ensuring it can effectively handle increasing user demands and data inputs. Our project represents a significant advancement in the integration of machine learning with web technology to address critical healthcare challenges. By offering a user-friendly platform for kidney disease prediction, we aim to facilitate early detection, personalized treatment strategies, and improved patient outcomes. Moving forward, we anticipate further iterations and enhancements based on user feedback to continually improve the system’s effectiveness and utility in clinical practice. The continuous refinement of the user interface, incorporation of additional features for data interpretation, and integration of advanced security measures will be key areas of focus. Additionally, we envision expanding the scope of the system to incorporate real-time data updates, enabling timely interventions and personalized healthcare recommendations. In conclusion, our project not only represents a significant stepforward in the realm of kidney disease prediction but also underscores the potential of integrating machine learning with web technology to address complex healthcare challenges. Through ongoing development and refinement, we aim to make meaningful contributions to improving patient care and outcomes in the field of nephrology.

Published Paper   E-Certificate

Boddeda Ajay, Aravapalli Jabali Rushi, & Kantamneni Abhinay Siddhartha (March-2024). Kidney Disease Prediction. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(3), e599-e603. https://ijnrd.org/papers/IJNRD2403472.pdf

Volume 9 Issue 3, March-2024

Pages : e599-e603

Paper Reg. ID: IJNRD_215992

Published Paper Id: IJNRD2403472

Research Area: Computer Science & Technology 

Author Type: Indian Author

Country: Visakhapatnam, Andhra Pradesh,, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2403472.pdf

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