Publication Type : Conference Paper
Publisher : Springer Nature Singapore
Source : Smart Innovation, Systems and Technologies
Url : https://doi.org/10.1007/978-981-97-5081-8_26
Campus : Amritapuri
School : School of Computing
Department : Computer Science and Engineering
Year : 2024
Abstract : Alcoholic Fatty Liver Disease AFLD is a condition in India that sometimes leads to the need for liver transplantation in severe cases. However, accurately predicting the success of liver transplantation Patients with AFLD can be challenging due to the country’s distinct genetic composition, lifestyle differences, and healthcare characteristics. To resolve this problem, we provide a deep learning-based technique that considers categorical and numerical information specifically focused on India patient population. Analyzing a data set of 1000 cases and using a multi-modal deep learning algorithm, our proposed model achieved an accuracy of 95%, successfully identifying the specific risk factors unique to India. This research also focused on the importance of data privacy. Our AI-driven healthcare solution shows promise in diagnosing the various health issues related to liver diseases.
Cite this Research Publication : Devi Rajeev, S. Remya, Anand Nayyar, Krishnanunni Nair, Deep Learning-Driven Graft Survival Prediction for Indian AFLD Patients Following Liver Transplantation, Smart Innovation, Systems and Technologies, Springer Nature Singapore, 2024, https://doi.org/10.1007/978-981-97-5081-8_26