An Artificial Neural Network Approach to Predicting Stroke in Postmenopausal Women
Abstract
No Abstract
1. Bushnell CD, Hurn P, Colton C, et al (2006). Advancing the study of stroke in women: summary and recommendations for fu-ture research from an NINDS-sponsored multidisciplinary working group. Stroke, 37(9): 2387-99.
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4. Hu FB, Grodstein F, Hennekens CH, Col-ditz GA, Johnson M, Manson JE, Ros-ner B, Stampfer MJ (1999). Age at natural menopause and risk of cardiovascular disease. Arch Intern Med, 159(10): 1061-6.
5. National Stroke Association (2019). Women and stroke. American Heart Association, USA. www.stroke.org
6. LeCun Y, Bengio Y, Hinton G (2015). Deep learning. Nature, 521(7553): 436-44.
7. Li H, Luo M, Zheng J, Luo J, Zeng R, Feng N, Du Q, Fang J (2017). An artificial neu-ral network prediction model of congeni-tal heart disease based on risk factors: A hospital-based case-control study. Medicine, 96(6): e6090.
8. Huang SQ, Xu YH, Yue L, Wei S, Liu L, Gan X, Zhou S, Nie S (2010). Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area. Hypertens Res, 33(7): 722-6.
9. Hsieh MH, Sun LM, Lin CL, Hsieh MJ, Hsu CY, Kao CH (2018). Development of a prediction model for pancreatic cancer in patients with type 2 diabetes using logistic regression and artificial neural network models. Cancer Manag Res, 10: 6317-24.
10. Rasmy L, Wu Y, Wang N, et al (2018). A study of generalizability of recurrent neu-ral network-based predictive models for heart failure onset risk using a large and heterogeneous EHR data set. J Biomed In-form, 84: 11-16.
2. Atsma F, Bartelink ML, Grobbee DE, van der Schouw YT (2006). Postmenopausal status and early menopause as independ-ent risk factors for cardiovascular disease: a meta-analysis. Menopause, 13(2): 265-79.
3. Korean Statistical Information Service (2018). State of major illnesses and symptoms by province, grade, qualification, 2017. Na-tional Statistical Office, Republic of Korea. www.koisis.kr
4. Hu FB, Grodstein F, Hennekens CH, Col-ditz GA, Johnson M, Manson JE, Ros-ner B, Stampfer MJ (1999). Age at natural menopause and risk of cardiovascular disease. Arch Intern Med, 159(10): 1061-6.
5. National Stroke Association (2019). Women and stroke. American Heart Association, USA. www.stroke.org
6. LeCun Y, Bengio Y, Hinton G (2015). Deep learning. Nature, 521(7553): 436-44.
7. Li H, Luo M, Zheng J, Luo J, Zeng R, Feng N, Du Q, Fang J (2017). An artificial neu-ral network prediction model of congeni-tal heart disease based on risk factors: A hospital-based case-control study. Medicine, 96(6): e6090.
8. Huang SQ, Xu YH, Yue L, Wei S, Liu L, Gan X, Zhou S, Nie S (2010). Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area. Hypertens Res, 33(7): 722-6.
9. Hsieh MH, Sun LM, Lin CL, Hsieh MJ, Hsu CY, Kao CH (2018). Development of a prediction model for pancreatic cancer in patients with type 2 diabetes using logistic regression and artificial neural network models. Cancer Manag Res, 10: 6317-24.
10. Rasmy L, Wu Y, Wang N, et al (2018). A study of generalizability of recurrent neu-ral network-based predictive models for heart failure onset risk using a large and heterogeneous EHR data set. J Biomed In-form, 84: 11-16.
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| Issue | Vol 51 No 4 (2022) | |
| Section | Letter to the Editor | |
| DOI | https://doi.org/10.18502/ijph.v51i4.9261 | |
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Park H, Kim K. An Artificial Neural Network Approach to Predicting Stroke in Postmenopausal Women. Iran J Public Health. 2022;51(4):964-966.
