A Hybrid Optimized Machine Learning Model for Non-Invasive Procedures Based Early Diagnosis of Hepatocellular Carcinoma Using Novel Biomarker
Keywords:
Hepatocellular carcinoma, detection, machine learning, hybrid, PIVKA II, Lasso, PSO.Abstract
The primary liver cancer, Hepatocellular Carcinoma (HCC) is found mainly in the alcoholic and non-alcoholic patients. The detection of the disease in the early stage is having a vital role as it is having less or no symptoms till the final stage of the progress. In this paper, a hybrid machine learning model is presented for the detection of HCC in early stage. Here we use a novel biomarker PIVKA II (protein-induced by vitamin K absence). We can use it in combination with traditional routine biomarker Alphafeto protein as well as others. The paper proposes a metaheuristic optimization-based embedded method of feature selection using Least Absolute Shrinkage and Selection Operator (LASSO) blended with Particle Swarm Optimization (PSO). Classification done with three different algorithms called Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and K Nearest Neighbor (KNN) algorithms. First model emphasizes on the lasso regression and cross validation with the classifiers. Second model proposes PSO optimized LASSO with the classifiers. The proposed second model combines the advantages of both embedded LASSO and PSO algorithms to obtain the best classification results. Using the latter model, among three classification algorithms, LASSO and PSO optimized SVM showed much elevated level of classification results. The proposed LASSO and PSO based SVM showed an elevated accuracy rate of 88.1%, f1 score 91.2% and true positive rate 91.7%.
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