Development and validation of a cardiovascular risk prediction model for Sri Lankans using machine learning

Sri Lankans do not have a specific cardiovascular (CV) risk prediction model and therefore, World Health Organization(WHO) risk charts developed for the Southeast Asia Region are being used. We aimed to develop a CV risk prediction model specific for Sri Lankans using machine learning (ML) of data o...

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Veröffentlicht in:	PloS one 2024-10, Vol.19 (10), p.e0309843
Hauptverfasser:	Mettananda, Chamila, Sanjeewa, Isuru, Benthota Arachchi, Tinul, Wijesooriya, Avishka, Chandrasena, Chiranjaya, Weerasinghe, Tolani, Solangaarachchige, Maheeka, Ranasinghe, Achila, Elpitiya, Isuru, Sammandapperuma, Rashmi, Kurukulasooriya, Sujeewani, Ranawaka, Udaya, Pathmeswaran, Arunasalam, Kasturiratne, Anuradhini, Kato, Nei, Wickramasinghe, Rajitha, Haddela, Prasanna, de Silva, Janaka
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Adult Aged Biology and Life Sciences Blood cholesterol Blood pressure Cardiovascular diseases Cardiovascular Diseases - epidemiology Charts Cholesterol Cohort Studies Computer and Information Sciences Datasets Diabetes mellitus Epidemiology Ethics Examinations External pressure Female Health risks Heart Disease Risk Factors Hospitals Humans Learning algorithms Machine Learning Male Medicine and Health Sciences Metabolism Methods Middle Aged People and places Performance prediction Physical Sciences Prediction models Prevention Primary care Research and Analysis Methods Risk Risk Assessment - methods Risk Factors ROC Curve Sri Lanka - epidemiology Testing Validity
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