International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


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Research Paper | Information Technology | United States of America | Volume 12 Issue 11, November 2023


Revolutionizing Liver Disease Diagnosis: AI-Powered Detection and Diagnosis

Mayur Rele | Dipti Patil


Abstract: Liver disease is a global health concern of signi?cant magnitude, necessitating early and accurate diagnosis for e?ective treatment. Conventional diagnostic methods are often laborious and susceptible to human errors. This research paper embarks on a journey to harness the potential of diverse Machine Learning (ML) models, encompassing Logistic Regression, Random Forest, K-Nearest Neighbors (KNN), Support Vector Classi?cation (SVC), and XGBoost, to revolutionize the landscape of liver disease diagnosis. Our primary objective is to create a robust framework for the early detection of liver diseases, transcending the limitations of traditional diagnostic approaches. Liver diseases encompass a spectrum of conditions, from hepatitis to liver cancer, which collectively a?ect millions of people globally. The importance of early diagnosis cannot be overstated, as it allows for timely interventions and signi?cantly enhances patient outcomes. Traditional diagnostic methods, such as liver function tests and biopsy, often involve prolonged processing times and can be susceptible to variations in interpretation. Leveraging the capabilities of AI and ML techniques in diagnosing liver diseases o?ers the potential for greater accuracy, speed, and cost-e?ectiveness. In this paper, we delve into the core principles, bene?ts, challenges, and applications of the ML above techniques for liver disease diagnosis. Logistic Regression is explored for its ability to model binary outcomes and interpretability. Random Forest is highlighted for its ensemble learning capacity and resistance to over?tting. K-Nearest Neighbors (KNN) is a simple yet e?ective classi?cation algorithm bene?cial for pattern recognition. Support Vector Classi?cation (SVC) is introduced for its ability to ?nd optimal hyperplanes, and XGBoost is discussed for its e?ciency and predictive power. The advantages of integrating AI and ML in liver disease diagnosis are manifold, including enhanced diagnostic accuracy, improved e?ciency in processing vast patient data, optimal feature selection, and scalability to handle diverse liver diseases. However, data quality, model interpretability, and ethical considerations must be addressed as these models gain prominence in the ?eld. The future of liver disease diagnosis holds promise in areas such as early detection, personalized medicine, telemedicine, and predictive analysis, which can signi?cantly enhance patient care and management. As technology advances and data quality improves, our research project represents a critical step toward more accurate and timely liver disease detection, ushering in a new era of improved patient care and outcomes in liver disease diagnosis.


Keywords: Liver disease diagnosis, Machine learning models, Support Vector Classi?cation (SVC), Data preprocessing, Gender disparities, Data quality enhancement, Model interpretability, Clinical validation, Ethical considerations, Healthcare innovation


Edition: Volume 12 Issue 11, November 2023,


Pages: 401 - 407


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