LIVER DISEASE PREDICTION USING DEEP LEARNING
A.Vijay, A.V.S Pavan, A.Surya Pavan, A.Sri Charan, Veena Rani
Malla Reddy University
Hyderabad
ABSTRCT:
Liver disease is a significant global health concern, and timely and accurate prediction of liver disease can greatly impact patient outcomes. In recent years, deep learning techniques have shown promise in various medical applications. This study aims to develop a deep learning-based predictive model for liver disease diagnosis. The proposed model utilizes a large dataset of patient information, including demographic data, clinical history, laboratory test results, and medical imaging studies. The data is preprocessed to remove noise, handle missing values, and normalize the features. A deep learning architecture, such as a convolutional neural network (CNN) or recurrent neural network (RNN), is designed and trained using the dataset. During the training phase, the model learns from the data to identify relevant patterns and relationships between the input features and the presence or severity of liver disease. The model's parameters are optimized through backpropagation to minimize prediction error or maximize a defined performance metric.After training, the model is evaluated on a separate test dataset to assess its performance in predicting liver disease. Performance metrics such as accuracy, precision, recall, and AUC-ROC are computed to evaluate the model's effectiveness. Liver disease prediction using deep learning has the potential to assist healthcare professionals in making timely diagnoses, facilitating early intervention and treatment planning. Furthermore, these models may uncover novel risk factors or biomarkers associated with liver disease, leading to advancements in diagnostic and therapeutic approaches.
However, challenges exist in obtaining large, high-quality labeled datasets and interpreting the black-box nature of deep learning models. Future research should focus on addressing these limitations and further refining deep learning techniques for liver disease prediction.
Overall, this study demonstrates the potential of deep learning in liver disease prediction, with the hope of improving diagnostic accuracy and ultimately enhancing patient outcomes..
