Biomarkers for minimally invasive multigrade liver cancer diagnosis using integrative heterogeneous models and evolutionary algorithms.
Liver cancer, a leading global cause of death, requires early and accurate diagnosis for better treatment outcomes and reduced mortality rates. This retrospective cohort study presents a machine learning-based predictive framework utilizing a combined panel of minimally invasive circulating biomarkers for early diagnosis and multi-grade prediction of liver cancer. This curated dataset is composed of urine/blood biomarkers like β-hCG, PD-L1 and Alpha-Fetoprotein. A hybrid and heterogeneous ensemble learning-based prediction technique has been proposed for the thorough analysis of collected data about the subject and associated biomarkers from highly diverse and broad age groups. The model's heterogeneity involved applying multiple distinct learning algorithms to the same training dataset, with the outcomes of each classifier used to expand the feature space and guide decision-making in subsequent stages. The proposed ensemble model is characterized by an iterative methodology incorporating both bagging and boosting techniques, ensuring adaptability and sustained enhancement, thereby surmounting limitations inherent in conventional static ensembles. The best hyperparameters using evolutionary algorithm achieved an accuracy of 93.33% and an F1-score of 94.29% on the test samples. The developed model accurately predicts liver cancer risk and grade, outperforming conventional methods. This AI model minimizes invasive procedures and healthcare costs, enhancing overall public health. The proposed minimally invasive biomarker-based framework may be particularly useful in resource-limited clinical settings where advanced imaging infrastructure and invasive diagnostic procedures are not readily accessible.