International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 05 | May 2024
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p-ISSN: 2395-0072
Liver Disease Analysis Using Machine Learning Trupti Kherde1, Supriya Jadhav2, Yash Chougule3, Rakshita Khidbide4, Riya Mohite5 1Professor, Dept. of Computer Engineering, Pimpri Chinchwad College Of Engineering And Research,
Maharashtra, India
2Student, Dept. of Computer Engineering, Pimpri Chinchwad College Of Engineering And Research,
Maharashtra, India
3Student, Dept. of Computer Engineering, Pimpri Chinchwad College Of Engineering And Research,
Maharashtra, India
4Student, Dept. of Computer Engineering, Pimpri Chinchwad College Of Engineering And Research,
Maharashtra, India
5Student, Dept. of Computer Engineering, Pimpri Chinchwad College Of Engineering And Research,
Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------hepatitis, and liver cancer. The utilization of machine Abstract - The increasing ubiquity of multimedia data,
learning allows for the analysis of diverse patient data sets, encompassing genetic data, laboratory test results, and patient records.
spanning text, audio, and video, has necessitated the development of effective summarization techniques. In our digital era, where information overload is prevalent, the ability to generate concise and coherent summaries is paramount. Deep Learning (DL) techniques have emerged as potent tools for addressing the multifaceted challenges inherent in multimedia summarization. This study focuses on summarization methods that leverage DL to extract meaningful content from diverse multimedia formats. The objective is to provide a comprehensive overview of the state-of-the-art DL techniques employed in summarizing multimedia data. By exploring various multimedia formats and their associated challenges, this research contributes to the evolving landscape of multimedia summarization, offering insights into its applications and future potential.
The objectives of this study are manifold. Firstly, we aim to explore how machine learning algorithms can detect early symptoms and risk factors for liver disorders, enabling timely interventions and improving patient outcomes. Additionally, we seek to investigate how machine learning techniques can aid in the classification of different liver diseases based on distinct patterns in patient data. This classification is crucial for tailoring treatment plans and predicting disease progression accurately. Moreover, our research endeavors to develop predictive models that assess an individual's risk of developing or exacerbating liver disease, informing personalized preventive measures and therapies. Furthermore, we aim to optimize treatment strategies by evaluating the efficacy of various therapies across diverse patient cohorts, leading to more targeted and efficient interventions.
Key Words: Machine Learning, Classification, Random Forest, Naive Bayes, Regression
1.INTRODUCTION
By addressing these objectives, our research aims to contribute to the advancement of medical practices in liver disease management, ultimately leading to improved patient care and outcomes.
The liver, an indispensable organ in the human body, plays a pivotal role in various critical processes such as metabolism, detoxification, and nutrient storage. However, liver illnesses pose significant threats to overall health and well-being. Early detection and intervention are paramount for effectively managing these conditions. In recent years, artificial intelligence (AI), particularly its subset of machine learning, has emerged as a potent tool in the medical field, offering promising avenues for analyzing complex medical data and predicting disease outcomes, particularly those associated with liver health.
1.1 Methodology In this study, we employ a Random Forest classifier to analyze a dataset comprising clinical features, laboratory test results, and patient demographics. We preprocess the data to handle missing values, normalize features, and ensure compatibility with the algorithm. Hyperparameters such as the number of trees, maximum tree depth, and maximum features considered for splitting are optimized using grid search cross-validation. The trained model is evaluated using metrics such as accuracy, precision, recall, and F1-score to assess its performance in predicting liver disease outcomes.
This research project delves into the application of machine learning algorithms in analyzing medical data related to liver diseases, aiming to improve early detection and prognosis. By leveraging cutting-edge computational methods and predictive modeling, we aim to enhance our understanding of liver illnesses, including cirrhosis,
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