ABSTRACT
The paper presents the occurrence of high blood pressure for three categories of persons, namely the normal population, patients with hypertension, and pregnant women with or without hypertension and preeclampsia in the context of a proposed healthcare system. The input data is public and it is provided by the Massachusetts University Amherst and National Health and Nutrition Survey. The data have been processed using four machine learning classifiers, namely Gaussian Naive Bayes, Logistic regression, Random Forest and Support Vector Machines. The criteria of the analysis are based on the absence or occurrence of hypertension, precision, recall, F1-score and population indicators. For the analyzed cases, the persons who did not present high blood pressure have been correctly detected, while half of the cases for which hypertension was present, proved to be true. As a conclusion, the automatic detection of healthcare parameters that exceed the allowed thresholds and are determined based on machine learning proves to be important for monitoring and prevention of critical health issues of the persons who belong to diverse categories.
- G. M. Peres, M. Mariana and E. Cairrao (2018). Pre-Eclampsia and Eclampsia: An Update on the Pharmacological Treatment Applied in Portugal. Journal of Cardiovascular Development and Disease, 5(1).Google ScholarCross Ref
- A. L. Tranquilli, G. Dekker, L. Magee et al. (2014). The classification, diagnosis and management of the hypertensive disorders of pregnancy: a revised statement from the ISSHP. Pregnancy Hypertension: An International Journal of Women's Cardiovascular Health, 4(2), 97--104.Google Scholar
- A. Mammaro, S. Carrara, A. Cavaliere et al. (2009). Hypertensive Disorders of Pregnancy. Journal of Prenatal Medicine, 3(1), 1--5.Google Scholar
- C. Visintin, M. A. Mugglestone, M. Q. Almerie et al. (2010). Management of hypertensive disorders during pregnancy: summary of NICE guidance. British Medical Journal, 341.Google Scholar
- J. Mayrink, M. L. Costa and J. G. Cecatti (2018). Preeclampsia in 2018: Revisiting Concepts, Physiopathology, and Prediction. The Scientific World Journal.Google Scholar
- V. D. Garovic and P. August (2013). Preeclampsia and the future risk of hypertension: the pregnant evidence. Current hypertension reports, 15(2), 114--121.Google Scholar
- F. A. English, L. C. Kenny and F. P. McCarthy (2015). Risk factors and effective management of preeclampsia. Integrated blood pressure control, 8, 7--12.Google Scholar
- I. Marin, A. Vasilateanu, B. Pavaloiu and N. Goga (2018). User Requirements and Analysis of Preeclampsia Detection done through a Smart Bracelet. 12th International Technology, Education and Development Conference, 8567--8576, 2018.Google ScholarCross Ref
- A. Jeyabalan (2014). Epidemiology of preeclampsia: Impact of obesity. Nutrition Reviews, 71, 2014.Google Scholar
- J. O. Lo, J. F. Mission, A. B. Caughey (2013). Hypertensive disease of pregnancy and maternal mortality. Current opinion in obstetrics & gynecology, 25, 2, 124--132, 2013.Google Scholar
- Institute for Quality and Efficiency in Health Care (IQWiG), What is blood pressure and how can I measure it?, https://www.ncbi.nlm.nih.gov/books/NBK279251/. Accessed: 2019-04-02.Google Scholar
- C. J. Rodriguez, K. Swett et al. (2014).Systolic Blood Pressure Levels Among Adults With Hypertension and Incident Cardiovascular Events. Jama Internal Medicine, 174, 8, 1252--1261.Google ScholarCross Ref
- L. C. Kovell, S. P. Juraschek and S. D. Russell (2015). Stage A Heart Failure Is Not Adequately Recognized in US Adults: Analysis of the National Health and Nutrition Examination Surveys, 2007-2010. PLoS One, 10, 7.Google Scholar
- D. Shimbo, M. Abdalla, L. Falzon, R. R. Townsend and P. Muntner (2016). Role of Ambulatory and Home Blood Pressure Monitoring in Clinical Practice: A Narrative Review. Annals of Internal Medicine, 163, 9, 691--700.Google ScholarCross Ref
- T. G. Pickering, J. E. Hall, L. J. Appel et al. (2005). Recommendations for Blood Pressure Measurement in Humans and Experimental Animals. Circulation, 111, 697--716.Google ScholarCross Ref
- I. Oladipo and A. Ayoade (2012). The effect of the first office blood pressure reading on hypertension-related clinical decisions. Cardiovascular Journal of Africa, 23, 8, 456--462.Google ScholarCross Ref
- R. Mustafa, S. Ahmed, A. Gupta and R. C. Venuto (2012). A Comprehensive Review of Hypertension in Pregnancy. Journal of Pregnancy.Google ScholarCross Ref
- V. D. Garovic, K. R. Bailey, E. Boerwinkle et al. (2010). Hypertension in pregnancy as a risk factor for cardiovascular disease later in life. Journal of Hypertension, 28, 4, 826--833.Google ScholarCross Ref
- R. A. Salam, J. K. Das, A. Ali, S. Bhaumik and Z. S. Lassi (2015). Diagnosis and management of preeclampsia in community settings in low and middle-income countries. Journal of Family Medicine and Primary Care, 4, 4, 501--506.Google ScholarCross Ref
- F. Assarzadegan, M. Asadollahi, O. Hesami, O. Aryani, B. Mansouri and N. B. Moghadam (2013). Secondary headaches attributed to arterial hypertension. Iranian Journal of Neurology, 12, 3, 106--110.Google Scholar
- P. R. James and C. Nelson-Piercy (2004). Management of hypertension before, during, and after pregnancy. Heart, 90, 12, 1499--1504.Google ScholarCross Ref
- E. J. Lamb, F. MacKenzie and P. E. Stevens (2009). How should proteinuria be detected and measured?. Annals of Clinical Biochemistry, 46, 205--217.Google ScholarCross Ref
- N. Kallioinen, A. Hill, M. S. Horswill et al. (2017). Sources of inaccuracy in the measurement of adult patients' resting blood pressure in clinical settings: a systematic review. Journal of Hypertension, 35, 3, 421--441.Google ScholarCross Ref
- S. Sheikh, A. D. Sinha and R. Agarwal (2011). Home Blood Pressure Monitoring: How Good a Predictor of Long-Term Risk?. Current Hypertension Reports, 13, 3, 192--199.Google ScholarCross Ref
- O. Friedman and A. G. Logan (2009). Nocturnal blood pressure profiles among normotensive, controlled hypertensive and refractory hypertensive subjects. The Canadian Journal of Cardiology, 25, 9, 312--316.Google ScholarCross Ref
- E. M. Frese, A. Fick and H. S. Sadowsky (2011). Blood Pressure Measurement Guidelines for Physical Therapists. Cardiopulmonary Physical Therapy Journal, 22, 2, 5--12.Google ScholarCross Ref
- nhanes3: NHANES III data: 2019. https://rdrr.io/rforge/LogisticDx/man/nhanes3.html#heading-4. Accessed: 2019-04-10.Google Scholar
- G. Bonaccorso (2017). Machine Learning Algorithms. Packt Publishing Ltd..Google Scholar
Index Terms
- Hypertension Detection based on Machine Learning
Recommendations
Machine Learning Approach for Pre-Eclampsia Risk Factors Association
Goodtechs '18: Proceedings of the 4th EAI International Conference on Smart Objects and Technologies for Social GoodThe preeclampsia/eclampsia syndrome is a multisystem disorder that usually includes cardiovascular changes, hematologic abnormalities, hepatic and renal impairment, and neurologic or cerebral manifestations. Preeclampsia (PE) is a clinical syndrome that ...
Diabetes Prediction using Machine Learning Algorithms
AbstractDiabetes Mellitus is among critical diseases and lots of people are suffering from this disease. Age, obesity, lack of exercise, hereditary diabetes, living style, bad diet, high blood pressure, etc. can cause Diabetes Mellitus. People having ...
A Machine Learning Solution for Bed Occupancy Issue for Smart Healthcare Sector
AbstractThe health care domain is a culmination and emergence of many other economic sectors that give different services from patient treatment to healing, protective, rehabilitation, and palliative care. The GDP consumes to facilitate health in terms of ...
Comments