Diabetic and Kidney Disease Prediction in Human based on Their Age Group using C4.5 Decision Tree Algorithm in Python

Authors

  • Sibi R
  • Valarmathi V
  • Kavitha S K

Abstract

An immense measure of information is produced inside the fields of tending and prescription; specialists need to make an on the spot contact with patients to work out the wounds and sicknesses. This analysis highlights the appliance of classifying and predicting a selected disease by implementing the operations on medical information generated within the field of medical and healthcare. In this study an efficient c4.5 algorithmic rule is employed for prediction of a particular disease by training it on a group of information before implementation. Wrong clinical decisions taken by medical practitioners will cause any hurt or lead to serious loss of life of a patient that is tough to afford by any hospital. To accumulate an explicit and value effective treatment, technology based mostly data mining systems can be created to make worthy decisions. The main aim of this analysis is to make a basic decision network which can determine and extract previously unseen patterns, relations and concepts connected with multiple disease from a historical information records of specified multiple diseases. The decisions taken by medical practitioners with the help of technology can result in effective and low value treatments. There is an insufficiency of technology and analysis system and methods to get connections, concepts and patterns in the medical information. Data mining is an engineering study of extracting previously undiscovered patterns from a specific set of information. In this study, data mining methods namely, C4.5 algorithms are used for detecting the disease in human based on their age group.

Downloads

Published

2020-02-07

Issue

Section

Articles