PROJECT DETAILS

atty liver disease

Classification of fatty liver disease based on supervised learning and optimization algorithm


Rs.7,000


  •  The proposed framework will employ supervised learning algorithms and optimization techniques to enhance the accuracy of FLD subtype classification, ensuring more precise diagnosis.
  •   The integration of optimization algorithms will enable automatic feature selection, reducing the dimensionality of input data and improving computational efficiency.
  •   By analyzing the relationships between input features and FLD subtypes, the classification framework will provide valuable insights into the underlying mechanisms of the disease, aiding in understanding the factors contributing to different FLD subtypes.
  •   The proposed framework will be applicable to diverse datasets. Additionally, the integration of optimization algorithms will enable scalability, accommodating larger datasets and adapting to future advancements in FLD research.

  • Description

Fatty liver disease (FLD) is a prevalent medical condition characterized by the accumulation of excess fat in the liver, leading to liver dysfunction and potentially severe complications. Accurate classification of FLD subtypes is crucial for effective diagnosis and treatment planning. In this study, we propose a classification framework based on supervised learning and optimization algorithms to improve the accuracy and efficiency of FLD subtype identification. We utilize a dataset comprising various clinical features, imaging data, and histopathological findings to train and evaluate our classification model. The supervised learning component employs existing machine learning algorithms to learn the complex relationships between input features and FLD subtypes. Additionally, we integrate optimization algorithms, to enhance the model's performance by optimizing hyperparameters and feature selection.