Paper Title
PROACTIVE HEALTHCARE MACHINE LEARNING IN JAUNDICE RISK PREDICTION
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Registration ID: IJNRD_306232
Published ID: IJNRD2504597
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Keywords
Proactive healthcare, machine learning, junction risk prediction, predictive analytics, healthcare, early diagnosis, clinical decision support, risk assessment, medical data mining, health informatics, patient monitoring, preventive medicine, monitored learning, distinctive selection
Abstract
When predicting the yellow addiction phase using monitored machine learning, a model is developed in which patients are based on different stages of yellow skin based on the relevant features extracted from medical data. Yellow und is a disease characterized by yellow colouring of the skin and eyes due to increased levels of bilirubin in the blood, and can be reached at various stages. By using machine learning monitored techniques such as classification algorithms, this study aims to accurately predict the stage of yellowing in a patient using input features such as bilirubin levels, liver function tests, patient sensory statistics. The summary of this study title describes the importance of predicting yellow poisoning stages for timely interventions and treatment planning, how predictive models are developed, and the potential impact of healthcare providers on patient care optimization and management This study focuses on using monitored machine learning to classify patients at different stages based on clinical and diagnostic data. Yellowish is usually identified by the skin and yellow and eyes due to increased bilirubin levels, altering the severity and requiring a timely diagnosis for effective treatment. The purpose of this study is to create predictive models using algorithms such as decision-making, tree support, vector machine support, or logistics regression to analyse key features such as liver enzyme mirror, bilirubin concentration, patient age, and medical background. By accurately identifying the stages , this model can support clinicians with the fact that it can support diagnostic efficiency and enabling more targeted treatment strategies. Research highlights the importance of machine learning to improve diagnostic accuracy in hepatics and toned patient care.
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How To Cite (APA)
Durga K, Kannikanti Chetan Kumar, Rahul Poria, & V. Gowri (April-2025). PROACTIVE HEALTHCARE MACHINE LEARNING IN JAUNDICE RISK PREDICTION. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(4), f820-f825. https://ijnrd.org/papers/IJNRD2504597.pdf
Issue
Volume 10 Issue 4, April-2025
Pages : f820-f825
Other Publication Details
Paper Reg. ID: IJNRD_306232
Published Paper Id: IJNRD2504597
Research Area: Science and Technology
Author Type: Indian Author
Country: Chennai, Tamil Nadu, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2504597.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2504597
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