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DC Field | Value | Language |
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dc.contributor.author | Bandara, K.R.C | - |
dc.contributor.author | Dureksha, D.D.T.D | - |
dc.contributor.author | Pinidiya, S.C | - |
dc.contributor.author | Amarasinghe, R.M.G.H | - |
dc.contributor.author | Thelijjagoda, S | - |
dc.contributor.author | Kishara, J | - |
dc.date.accessioned | 2022-09-07T06:48:08Z | - |
dc.date.available | 2022-09-07T06:48:08Z | - |
dc.date.issued | 2022-07-18 | - |
dc.identifier.citation | K. R. C. Bandara, D. D. T. D. Dureksha, S. C. Pinidiya, R. M. G. H. Amarasinghe, S. Thelijjagoda and J. Kishara, "Healthy Heart – Heart Risk Prediction System on Personalized Guidance for Heart Patients," 2022 IEEE 7th International conference for Convergence in Technology (I2CT), 2022, pp. 1-8, doi: 10.1109/I2CT54291.2022.9824170. | en_US |
dc.identifier.isbn | 978-1-6654-2168-3 | - |
dc.identifier.uri | http://rda.sliit.lk/handle/123456789/2967 | - |
dc.description.abstract | Human heart is the principal part of the human body. Change in human lifestyle, work related stress and unhealthy food habits contribute to the increase in rate of numerous heart related diseases. In accordance with several research, various heart diseases have been the key reason for deaths in Sri Lanka. According to the 2018 records, stroke affected 31%, coronary heart disease affected 23%, and ischemic heart disease affected 14%. Therefore, there is a need for an automated system which will enhance medical efficiency and to identify such diseases in time for proper treatment. The proposed system takes physical and medical datasets of heart patients as manual input parameters and predicts the patient’s risk of having a heart disease. Prediction process grants the patient a risk level according to the heart condition and proposes a personalized daily guidance for the patient to avoid risks associated with, along with a meal planner, exercise scheduler and a stress releaser as well as alert the patient well in advance. The system will present an efficient technique of predicting heart diseases using machine learning approaches to analyze huge complex medical data. Some of the used algorithms are Random Forest, Logistic regression, Decision tree classifier etc... The research mainly aims to prevent the escalation of heart diseases in patients and lead them to a healthy lifestyle. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartofseries | 2022 IEEE 7th International conference for Convergence in Technology (I2CT); | - |
dc.subject | Healthy Heart | en_US |
dc.subject | Heart Risk | en_US |
dc.subject | Prediction System | en_US |
dc.subject | Personalized Guidance | en_US |
dc.subject | Heart Patients | en_US |
dc.title | Healthy Heart – Heart Risk Prediction System on Personalized Guidance for Heart Patients | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1109/I2CT54291.2022.9824170 | en_US |
Appears in Collections: | Department of Information Management Research Papers Research Papers - Dept of Information of Management Research Papers - SLIIT Staff Publications |
Files in This Item:
File | Description | Size | Format | |
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Healthy_Heart__Heart_Risk_Prediction_System_on_Personalized_Guidance_for_Heart_Patients.pdf Until 2050-12-31 | 1.62 MB | Adobe PDF | View/Open Request a copy |
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