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A Diabetic Healthcare Recommendation System

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Sanket Bankhele, Ashish Mhaske, Shikha Bhat, S. V. Shinde

Sanket Bankhele, Ashish Mhaske, Shikha Bhat and S V Shinde. A Diabetic Healthcare Recommendation System. International Journal of Computer Applications 167(5):14-18, June 2017. BibTeX

	author = {Sanket Bankhele and Ashish Mhaske and Shikha Bhat and S. V. Shinde},
	title = {A Diabetic Healthcare Recommendation System},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2017},
	volume = {167},
	number = {5},
	month = {Jun},
	year = {2017},
	issn = {0975-8887},
	pages = {14-18},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2017914246},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Recently there has been an increase in the number of diabetic patients at an alarming rate. Approximately 18 million people die from cardiovascular diseases every year where diabetes is one of the major factor. Treating diabetes and monitoring it is required to efficiently manage health conditions of diabetic patients. In this paper, an android application has been designed and developed that recommends probable medication, diet and exercise to help people manage their diabetes well. This system analyses the input parameters that are entered by the end user and provides personalized services for users in the form of recommendations for their diet, medicines and exercises. This android-based system can also remind users to carry out the recommendations, which are provided by the system. Other than the functional features, there are also several important non-functional features of the extensibility and the convenience for use. The recommendation is done using User based collaborative filtering, the system asks the user to enter a predetermined set of parameters which are matched with other patients parameters stored in the database, the database consists of past cases of patients who have been diagnosed with diabetes and treated, this matching is done using Pearson Correlation, the matched patient’s diet, exercise and workout is then recommended to the current user.


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User based Collaborative Filtering, Pearson Correlation Score