ENCL: An Extensive Learning Based Methodology to Predict Liver Disease by Using Enhanced Neural Classification Logic

Effective treatment and handling require precise forecasting and early diagnosis of liver diseases. This paper introduces the Enhanced Neural Classification Logic (ENCL), a novel deep learning framework that is intended to enhance the precision of liver disease predictions. The proposed model is cross-validated with the conventional learning approach, the Random Forest Classifier (RFC), to assess its efficiency. The proposed model ENCL utilizes advanced neural network architecture with optimizations a sequence, to fine-tune classification performance. Our approach includes a multi-layered neural network coupled with improved feature extraction, dynamic attention systems, and refined loss calculus for improving model sensitivity and specificity. We work on a substantially large dataset of the records pertinent to diseases of the liver, which contains both structured clinical data and unstructured imaging data for the assessment of ENCL. Results have indicated that ENCL is significantly better than traditional as well as state-of-the-art approaches regarding predictive reliability as well as classification accuracy. This paper is aimed at describing how the enhanced architecture of proposed Enhanced Neural Classification Logic can avail in order to address such complex issues of prediction within the medical domain towards effective care for the patients.