Mortality analysis of alcohol consumption using a hybrid machine learning model. (7th June 2021)

• Record Type: Journal Article
• Authors: Pragathi, P.; Rao, A. Nagaraja
• Is Part Of: International journal of engineering systems modelling and simulation. Volume 12:Number 2/3(2021)
• Page Start: 202
• Page End: 210
• Publication Date: 2021-06-07
• Subjects: chronic disease -- medical data analysis -- mortality rate -- linear regression -- correlation -- data mining; Engineering systems -- Computer simulation -- Periodicals; Engineering systems -- Mathematical models -- Periodicals; 620.0042
• Languages: English
• ISSNs: 1755-9758

Abstract

The day-to-day change and evolution of chronic conditions had a high impact on the medical field. Alcohol consumption is also another important and considerable cause of the occurrence of various chronic conditions. Generally, the data that is being collected during the diagnosis can be represented in various forms such as clinical values, reports, images, etc. There is a dire need of analysing this data to let the people and health centres/institutions knowledgeable about the criticality and effect of chronic conditions. This work mainly focuses on the analysis of the mortality rate that occurs due to alcohol consumption. To achieve this, K-means clustering with linear regression technique is proposed. The linear regression model is constructed to forecast the analysis of consumers on the whole. The simulation results evaluate the model and it is observed that the coefficient of determination exhibits that the constructed model is found to be fitting precisely.