Leading Cause of Death in the US -- Prediction and Visual Analysis

Document Type

Capstone

Lead Author Type

MBI Masters Student

Advisors

Dr. Guenter Tusch; tuschg@gvsu.edu

Embargo Period

8-23-2018

Abstract

PURPOSE: The prime purpose of the project is to provide visual analysis of diseases/human condition related deaths among the adults in the continental United States and provide accurate prediction for the future using Predictive Analytics and Machine Learning. The goals of the current project are accomplished by developing Shiny based interactive application to provide visualizations and predictions at the same time for nearly 13 diseases/conditions across all 50 States.

PROCEDURE: Shiny bases application was developed, and Machine Learning methodologies are used to train model to predict the outcome (as percentage rate per 100,000 individuals). Visual analysis was done in the form of simple line graphs using grammar of graphics library (ggplot). This library was considered due to its extensive usability and not very complicated methods of visualizations.

OUTCOME: The project outcome provides insights for people into the trends and make them learn about leading causes of death and possibly improve healthy living of the people so as to stay away from the risk and take necessary steps to improve healthy living. The project also provides trend analysis along with prediction of the near future based on the previous trends in that state/Region. Therefore, health department of that states can take necessary actions to improve the well being of their population and provide them with some initiatives (Ex: Healthy People 2020) to combat the risk of mortality through these diseases.

IMPACT: This project is closely associated with the Health Informatics with emphasis on Public Health Informatics. It helps people learn about incidence and severity of various diseases and gives an idea to the practitioner, leadership personnel, and health departments of states in the severity of diseases for the near future by Predictive Analytics and Machine Learning which gives nearly accurate predictions of various diseases and help them make appropriate decisions depending on the severity to be prepared to tackle the issue.

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