Big data dimensionality reduction-based supervised machine learning algorithms for NASH diagnosis

Identifying the Non-Alcoholic Steatohepatitis (NASH) that can cause liver failure-based morbidity remains a challenging research problem since there is no confirmed and effective approach for its early and accurate diagnosis yet. A large amount of medical data is collected to diagnose the NASH where the majority of them are redundant.

This paper initially focuses on selecting the most informative blood test data among the collected big data with the Pearson correlation statistical approach and modified Particle Swarm Optimization with Artificial Neural Networks (PSO-ANN) machine learning algorithm. Then, a gradient based Batch Least Squares (BLS) and a search-based Artificial Bee Colony (ABC) machine learning algorithms are implemented to optimize the NASH prediction models. Confirmed operational NASH diagnosis supervise the statistical and machine learning algorithms to develop accurate prediction models.

Two machine learning algorithms were trained and also validated with the varying number of selected input features. The results yielded that the trained BLS machine learning model is able to diagnose benign and malignant cases with 100% and 98% accuracies, respectively. The trained ABC machine learning algorithm diagnoses the benign and malignant cases with 90.5% and 94.3% accuracies, respectively.