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Chronic Disease Prediction by Analyzing Clinical Data

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2021
Pandey Himanshu Kumar Sachchidanand, Patel Jaxitkumar D., Pallavi Hire

Pandey Himanshu Kumar Sachchidanand, Patel Jaxitkumar D. and Pallavi Hire. Chronic Disease Prediction by Analyzing Clinical Data. International Journal of Computer Applications 183(12):8-12, June 2021. BibTeX

	author = {Pandey Himanshu Kumar Sachchidanand and Patel Jaxitkumar D. and Pallavi Hire},
	title = {Chronic Disease Prediction by Analyzing Clinical Data},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2021},
	volume = {183},
	number = {12},
	month = {Jun},
	year = {2021},
	issn = {0975-8887},
	pages = {8-12},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2021921395},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


This paper reviews various applications of machine learning and deep learning models and concepts in the diagnosis of chronic diseases. Patients suffering from these diseases need lifelong treatment. At the moment, Predictive models are frequently applied in the diagnosis and forecasting of these chronic diseases. In this study, the most common chronic diseases are been reviewed and analysed. This paper mostly focused on chronic diseases like Diabetes, Heart Disease and Skin Diseases. The outcomes of this journal suggest the diagnosis of chronic diseases, but there is no standard method to determine the best approach in real-time medical/clinical practice since these methods have their own advantages and disadvantages. Among the most commonly used methods, this paper considered Support Vector Machines (SVM), logistic regression (LR), clustering and convolutional neural network. These models are highly applicable in the classification, and diagnosis of chronic diseases and are expected to become more important in medical practiceshortly.


  1. Artificial Intelligence (AI) “”
  2. Support Vector Machine (SVM) “”
  3. Linear Regression (LR) “”
  4. Clustering Machine Learning “”
  5. Convolutional Neural Network (CNN) “”
  6. Dr Ravi S. Behra, PhD. Ritesh Jain. “IEEE 14th International conference on bioinformatics and bioengineering, on predictive modelling for wellness and chronic condition”. 
  7. A Study by Thomas Bodenheimer, MD, Edward H. Wagner, MD, MPH and Kevin Grumbach, MD.A on Aug.19,2010 JAMA.
  8. Allen M. Glasgow, Jill Weissberg-Benchell, W. Douglas Tynan, Sandra F. Epstein, Chris Driscoll, Jane Turek, EvBeliveau. "Readmissions of children with diabetes mellitus to a children's hospital"
  9. Beata track, Jonathan P. DeShazo, Chris Gennings, Juan L. Olmo, Sebastian Ventura, Krzysztof J. Cios and John N. Clore. "Impact of HbA1c measurement"
  10. Jill Koproski, CDE, ZoraydaPretto, MD and Leonid Poretsky. “The effects of intervention n hospitalized diabetic”.
  11. Yang Guo, GuohuaBai, Yan Hu School of Computing Blekinge Institute of Technology Karlskrona, Sweden.
  12. Kaggle for datasets“”


Medical/Clinical Data Analysing, Image Recognising, Convolutional Neural Network (CNN), Disease prediction models, chronic diseases (Diabetes, Heart Diseases, Skin Diseases), Accuracy of models.