Data Analytics and Mining in Healthcare with Emphasis on Causal Relationship Mining

Abstract

High volumes and varieties of data is piling every day from healthcare and related fields. This big data sources if managed and analysed properly will provide vital knowledge. Data mining and data analytics have been playing an important role in extracting useful information from healthcare and related data sources. The knowledge extracted from these data sources guiding patients and healthcare personnel towards improved health conditions. Analytical techniques from statistics, functionalities from data mining and machine learning already proved their capability with significant contributions to healthcare industry. The dominant functionality of data mining is classification which has been in use in mining healthcare data. Though classification is a good learning technique it may not provide a causation model which will be a reliable model for better decision making particularly in the medical field. The present models for causality have limitations in terms of scalability and reliability. The present study is targeted to study causal models for causal relationship mining. This study tried to conclude with some proposals for causal relationship discovery which are efficient, reliable and scalable. The proposed model is going to make use of some qualities of decision trees along with statistical tests and analytics. It is proposed to build the learning models on healthcare big data sources.