International Journal of Advanced Research in Computer and Communication Engineering

A monthly peer-reviewed online and print journal

ISSN Online 2278-1021
ISSN Print 2319-5940

Since 2012

Abstract: Artificial intelligence (AI) aims to impersonate human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytic techniques. Chronic Kidney Disease (CKD) damages the kidneys. Kidneys have the capability to eliminate waste from the body. If this situation occurs, the waste gets accumulated in the body. Chronic Kidney Disease (CKD) is one ailment which could devastate the human body. It can be prevented via examining few indicators like RBC count, specific gravity value, Blood Pressure (BP), albumin levels in urine, sugar content, anaemia and WBC count. Other conditions like coronary artery disease, Diabetes Mellitus (DM) and bacterial infections could directly affect the kidneys. [1] In this paper we have collected few samples from a public hospital and selected fields have been analysed for designing a prediction model for CKD. Data analysis and visualization are carried out to improve the statistical analysis of given data. Before AI systems can be deployed in health-care applications, they need to be ‘trained’ through data that are generated from clinical activities, such as screening, diagnosis, treatment assignment and so on, so that they can learn similar groups of subjects, associations between subject features and outcomes of interest. Logistic regression is carried out on the data since it contains lot of columns with categorical values. Accuracy, precision, and f1 score of the model have been measured. Various conclusions can be drawn from this interdependent data set and can be stored as historical data for future analysis.

Keywords: Chronic Kidney Disease (CKD), RBC count, specific gravity value, Blood Pressure (BP), albumin levels in urine, sugar content, anaemia, WBC count, Logistic regression, accuracy, precision, and f1 score, coronary artery disease, Diabetes Mellitus (DM) and bacterial infections, categorical values, data analysis and visualization.

 


PDF | DOI: 10.17148/IJARCCE.2019.8913

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