Machine learning in biomedical mining for disease detection

Abstract

Background and Objectives: Medicinal service providing industries creates a lot of complex information about patients, healing facilities assets, illnesses, diagnoses strategies, electronic patient's records and so forth. Text identification of handwritten medical transcripts is extremely difficult task in order to diagnose illness. Text mining is an adaptable innovation that can be connected to various distinctive assignments in medical domain. The objective of this review is to extract novel information from scientific text. Materials and Methods: In this paper, the authors have methodologically surveyed recent trends in text analytics with regard to developing application realm in the biomedical sciences. The materials and methods used are different types of machine learning classifiers and their respective variants. Results: In this, a study of results from past years have been investigated wherein, their approaches and their outcomes are compared through various evaluation measures. Conclusion: The survey provides a brief explanation of the stages which are involved in text analytics of medical records. Also it describes up-to-date machine learning techniques with their relevant parameters highlighting the recent trends which are followed by various researchers.