Chronic Kidney Disease: Data Analysis

Presenter

Cameron Shea Baron

Campus

Worcester State University

Sponsor

Elena Braynova, Department of Computer Science, Worcester State University

Schedule

Session 2, 11:30 AM - 12:15 PM [Schedule by Time][Poster Grid for Time/Location]

Location

Poster Board A29, Campus Center Auditorium, Row 2 (A21-A40) [Poster Location Map]

Abstract

Chronic Kidney Disease (CKD) occurs when the kidneys lose function over a period and are unable to continue working effectively to maintain a healthy system. CKD can affect patients of all ages and has an array of risk factors ranging from familial history to high blood pressure. If left untreated, CKD will continue to worsen until the patient enters kidney failure. Due to the time-sensitive nature of the disease, it is necessary to have an early diagnosis and to intervene before the end-stage is reached. Luckily, with continued efforts Data Science techniques help us to analyze CKD related data and to create a path toward timely CKD detection and proper management. In this project we focus on the CKD dataset visualization and its statistical analysis. We evaluate such attributes as a patient's medical lab values and disease history and use them in machine learning tasks. The study resulted in very high accuracy predictions aimed at CKD lab values, CKD diagnosis, and diagnoses of other conjoint patient diseases. The project results demonstrate the importance and value of interdisciplinary work, specifically between medicine and data science.  Some ideas for future work are discussed. 

Keywords

Data Analysis, Machine Learning, Chronic Kidney Disease, Data Science, Data Visualization

Research Area

Computer Science

SIMILAR ABSTRACTS (BY KEYWORD)