Large Scale Medical Data Mining for Accurate Diagnosis: A Blueprint

Abstract

Medical care and machine learning are associated together in the current era. For example, machine learning (ML) techniques support the medical diagnosis process/decision making on large scale of diseases. Advanced data mining techniques in diseases information processing context become essential. The present study covered several aspects of large scale knowledge mining for medical and diseases investigation. A genome-wide association study was reported including the interactions and relationships for the Alzheimer disease (AD). In addition, bioinformatics pipeline techniques were implied for matching genetic variations. Moreover, a novel ML approaches to construct a framework for large scale gene-gene interactions were addressed. Particle swam optimization (PSO) based cancer cytology is another discussed pivotal field. An assembly ML Random forest algorithm was mentioned as it was carried out to classify the features that are responsible for Bacterial vaginosis (BV) in vagina microbiome. Karhunen-Loeve transformation assures features finding from various level of ChIP-seq genome dataset. In the current work, some significant comparisons were conducted based on several ML techniques used for diagnosis medical datasets.