Predict the Readmission Patterns of Chronic Diseases Using Machine Learning

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

doi:10.21276/ijircst.2021.9.4.3

Abstract

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.

Keywords

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

References

Ryd´en L, Standl E, et al. Guidelines on diabetes, pre-diabetes and cardiovascular diseases: executive summary: The task force on diabetes and cardiovascular diseases of the European Society of Cardiology ESC) and of the European Association for the Study of Diabetes (EASD).
International Diabetes Federation. IDF diabetes atlas, 8th edition. http://diabetesatlas.org/IDF Diabetes Atlas 8e interactive , Belgium.
Diabetes Prediction using Machine Learning Algorithms, Aishwarya Mujumdara et al., International Conference On Recent Trends In Advanced Computing 2019, ICRTAC 2019, ScienceDirect, www.sciencedirect.com
Mani Butwall and Shraddha Kumar,” A Data Mining Approach for the Diagnosis of Diabetes Mellitus using Random Forest Classifier”,International Journal of Computer Applications, Volume 120 - Number 8,2015.
Carol M. Ashton, David H. Kuykendall, et al. The Association between the Quality of Inpatient Care and Early Readmission. Annals of Internal Medicine.
Sara J Healy, Dawn Black, et al. Inpatient diabetes education is associated with less frequent hospital readmission among patients with poor glycemic control. Diabetes Care, page DC 130108, 2013.
Reena Duggal, Suren Shukla, et al. Predictive risk modelling for early hospital readmission of patients with diabetes in India. International Journal of Diabetes in Developing Countries , 2016.
B. Strack, J. DeShazo, C. Gennings, J. Olmo, S. Ventura, K. Ciosand J. Clore, "Impact of HbA1c Measurement on Hospital Readmission Rates: Analysis of 70,000 Clinical Database Patient Records," BioMed Research International, vol. 2014.
Seckeler MD, Hoke TR. The worldwide epidemiology of acute rheumatic fever and rheumatic heart disease.
Gaziano TA, Bitton A, Anand S, Abrahams-Gessel S, Murphy A. Growing epidemic of coronary heart disease in low-and middle-income countries. Curr Probl Cardiol.
Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS ONE. 2017.
Ramalingam VV, Dandapath A, Raja MK. Heart disease prediction using machine learning techniques: a survey. Int J Eng Technol.

Cites this article as

K. Asha, K. Manisri, A. B. Rakesh, C. Rohitha, S. P. Kumar, "Predict the Readmission Patterns of Chronic Diseases Using Machine Learning", International Journal of Innovative Research in Engineering & Management (ijircst), Vol-9, Issue-4, Page No-14-17, 2021. Available from: https://doi.org/10.21276/ijircst.2021.9.4.3

Corresponding Author

Karukonda Asha

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

Download Full Paper

Download PDF

No. of Downloads: 42 | No. of Views: 1406

Advancing Education through VR: The Emerging Scope of Virtual Classrooms

Mohd Tanveer, Mohd Azat, Inayat Husain, Sakil Ahmad, Mohammad Aalam Khan.

May 2025 - Vol 13, Issue 3
Speech Recognition Technologies: Design, Challenges, and Real-World Applications

Maruti Maurya, Mohd Zaheer, Nawab Mohammad, Sadaf siddiqui, Mohd Zeeshan Khan, Mohd Ayan Akram.

May 2025 - Vol 13, Issue 3
Deep Learning-based Sentiment Analysis of Text using Long Short-Term Memory Networks

MD Shahid Ali, Saif Ali , Abdullah Parwez, Abu Sufiyan, Mohd Haroon.

May 2025 - Vol 13, Issue 3

Predict the Readmission Patterns of Chronic Diseases Using Machine Learning

Citations

Download Full Paper PDF

Total View 1406

Total Download 42