IJIRCST

Predict the Readmission Patterns of Chronic Diseases Using Machine Learning

Karukonda Asha | K Manisri | A Bhanu Rakesh | Ch Rohitha | S.V. Phani Kumar

Machine learning plays an essential role in predicting presence or obsence of locomotor disorders. The diseases like heart, diabetes, cancer, kidney we collect data and such information if predicted well in advance can provide important insights to doctors who can adapt their diagnosis and treatment per patient basis. Here comes to heart disease alternatively known as cardiovascular disease, encases various conditions that impact the heart is the primary basis of death worldwide over the span of the past few decades. when we come to the disease like diabetes which is among critical disease and lot of people are suffering from this disease. People having diabetes, kidney disease, heart stroke and nerve damage. Cancer disease is very dangerous and critical. Kidney disease also recognized as chronic renal disease, is an uncharacteristic functioning of kidney or a failure of renal function expanding over a period of months or years and it is detected during the screening of people who are known to be in threat by kidney problems. So, the early prediction is necessary in combating these diseases and to provide good treatment. For predicting these diseases in people, we are using machine learning techniques.

Locomotors disorders, Diagnosis, Heart, Diabetes, Lungs, Cancer and Kidney.

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[Karukonda Asha, K Manisri, A Bhanu Rakesh, Ch Rohitha, S.V. Phani Kumar (2021), Predict the Readmission Patterns of Chronic Diseases Using Machine Learning, International Journal of Innovative Research in Computer Science & Technology (IJIRCST), Vol-9, Issue-4, Page No-14-17], (ISSN 2347 - 5552). www.ijircst.org

Karukonda Asha

Student, Department of Computer Science & Engineering, Dhanekula Institute of Engineering and Technology, Vijayawada, Andhra Pradesh, India ( ashakarukonda24@gmail.com)

DOI:

DOI: 10.21276/ijircst.2021.9.4.3 DOI URL: https://doi.org/10.21276/ijircst.2021.9.4.3

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