LIVER PATIENT ANALYSIS & PREDICTION

Registration ID: IJNRD_216344

Published ID: IJNRD2403541

DOI: Click Here to Get

K NAGASAI , P. VISHNU VARDHAN , K. VISHNU , M.ISMAIL ZABIULLA , B. DINESH REDDY

Liver disease, Machine learning, Prediction, Healthcare analytics, Risk factors, Patient outcomes, Classification algorithms, Feature selection, Data preprocessing, Healthcare informatics

    This study focuses on the analysis and prediction of liver patient outcomes using machine learning techniques. Leveraging a dataset comprising various liver health indicators and patient attributes, we applied state-of-the-art machine learning algorithms to analyze patterns, identify risk factors, and predict patient outcomes. The research involved preprocessing the data, including handling missing values and normalization, followed by feature selection to identify the most relevant predictors. Evaluation metrics such as accuracy, precision, recall, and F1-score were used to assess the performance of the models. The results indicate promising predictive capabilities, with the potential to assist healthcare professionals in early diagnosis, risk stratification, and personalized treatment strategies for liver patients.

Published Paper   E-Certificate

K NAGASAI, P. VISHNU VARDHAN , K. VISHNU, M.ISMAIL ZABIULLA, & B. DINESH REDDY (March-2024). LIVER PATIENT ANALYSIS & PREDICTION. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(3), f363-f368. https://ijnrd.org/papers/IJNRD2403541.pdf

Volume 9 Issue 3, March-2024

Pages : f363-f368

Paper Reg. ID: IJNRD_216344

Published Paper Id: IJNRD2403541

Downloads: 000121979

Research Area: Computer Science & Technology 

Country: ANANTAPUR, ANDHRA PRADESH, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2403541.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2403541

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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