China 
Africa 
Explore chapters and articles related to this topic
SP-IMLA: Stroke Prediction Using an Integrated Machine-Learning Approach
Published in Sarvesh Tanwar, Sumit Badotra, Ajay Rana, Machine Learning, Blockchain, and Cyber Security in Smart Environments, 2023
Amit Bairwa, Satpal Singh Kushwaha, Vineeta Soni, Prashant Hemrajani, Sandeep Joshi, Pulkit Sharma
Stroke may strike anybody, regardless of colour, gender, or age; nevertheless, the odds of having a stroke rise if a person has specific risk factors for stroke [15]. According to studies, understanding personal risk and how to manage it can prevent 80% of strokes. There are two types of stroke risk factors: modifiable and non-modifiable [16]. Lifestyle risk factors and medical risk factors are two types of modifiable risk factors. Alcohol, smoking, physical inactivity, and obesity are all lifestyle risk factors that may be altered [17]. Medical risk factors that can typically be addressed are diabetes mellitus, high blood pressure, atrial fibrillation, and excessive cholesterol. A primary multi-centre case-control research study (INTERSTROKE) found eleven variables linked to 90% of stroke risk, half of which are modifiable [18]. Non-modifiable risk factors, on the other hand, cannot be changed, but they can assist in identifying those who are at risk for stroke [19] (Figures 6.7–6.12).
Earlier Prediction of Cardiovascular Disease Using IoT and Deep Learning Approaches
Published in J. Dinesh Peter, Steven Lawrence Fernandes, Carlos Eduardo Thomaz, Advances in Computerized Analysis in Clinical and Medical Imaging, 2019
An estimated 17.5 million people died from cardiovascular disease in 2012, representing 31% of global deaths. 17–45% patients die within the first year and the remaining die in the fifth year [3]. Based on the statistical report of American Heart Association (AHA), one out of three deaths is mainly due to cardiovascular disease. Cardiovascular disease is caused by a disorder of blood vessels in the heart. When the supply of blood to the heart is blocked, it often results in heart attack. This disease is associated with buildup of fats deposits in the arteries, thus increases risk. The factors that increase the risk of heart attack are high cholesterol level, diabetes, age, obesity, work stress, angina, previous heart attack, etc. It can be prevented by taking care of risk factors such as tobacco use, obesity an unhealthy diet, physical inactivity, and alcohol use. It is important to predict when a person is at high risk. Medical diagnosis needs to be carried out effectively and precisely. It would be better when it is automatically done. Internet of Things (IoT) and deep learning approaches can be used for the prediction of cardiovascular disorder.
Historical Development of HRV Analysis
Published in Herbert F. Jelinek, David J. Cornforth, Ahsan H. Khandoker, ECG Time Series Variability Analysis, 2017
One of the most important risk factors for cardiovascular diseases is hypertension. This association has been found in various studies. Autonomic dysfunction has been demonstrated before hypertension is established as well as in its early stages (Singh et al. 1998). Patients with hypertension exhibit increased LF power and reduced circadian patterns (Guzzetti et al. 1991). Langewitz et al. (1994) found decreased HF power and also a loss of circadian rhythm. The Framingham Heart Study (Singh et al. 1998) is one of the major studies which found reduced HRV in men and women with systemic hypertension and that LF power of HRV was associated with new onset hypertension in men.
Identifying heart disease risk factors from electronic health records using an ensemble of deep learning method
Published in IISE Transactions on Healthcare Systems Engineering, 2023
Linkai Luo, Yue Wang, Daniel Y. Mo
Heart disease is one of the leading causes of death worldwide. In the United States, heart disease and related diseases account for more than 600,000 deaths annually (CDC, 2022). The annual total cost due to heart diseases has been reported to reach 108.9 billion dollars, including medications, medical services, and lost productivity (Heidenreich et al., 2011). The development of heart disease is complicated and depends on numerous risk factors. The World Health Organization (WHO) defines these as “any attribute, characteristic or exposure of an individual that increases the likelihood of developing a disease or injury” (WHO, 2023). Medical research has indicated that risk factors related to heart disease include lifestyle factors such as smoking, hereditary factors such as family history of heart disease, and specific clinical conditions such as coronary artery disease (CAD), diabetes, obesity, hyperlipidemia, and hypertension (Dokken, 2008). Identifying and reducing potential risk factors are critically important for early prevention and treatment and to reduce the incidence of heart disease worldwide.
Pet ownership and risk of dying from cancer: observation from a nationally representative cohort
Published in International Journal of Environmental Health Research, 2020
Brian Buck, Kamalich Muniz-Rodriguez, Sarah Jillson, Li-Ting Huang, Atin Adhikari, Naduparambil Jacob, Yudan Wei, Jian Zhang
Behavioural risk factors play a crucial role in the development of cancer, especially alcohol drinking and cigarette smoking (Hamada et al. 2019; Rossi et al. 2018). We defined current alcohol drinking as ‘heavy’ if the respondents reported that, in the last 12 months, five or more drinks were consumed on more than 10 days, and ‘moderate’ if the number of days on which five drinks or more were consumed was ≤10 days, otherwise as ‘rare or lighter’. Tobacco smoking status was categorized as ‘never/rare’, ‘former’, and ‘current’. The formers and the currents were further classified as ‘moderate/heavy smokers’ if the respondents reported that 10 cigarettes or more were smoked per day, otherwise as ‘light smokers’. Serum cotinine, an alkaloid found in tobacco and also the predominant metabolite of nicotine, was used to control for underreporting of cigarette smoking or exposure to environmental tobacco smoke. Self-rated health condition has been associated with total mortality and may serve as a proxy of risk-adjustment effort (Lainscak et al. 2014). The history of asthma was used as the proxy of atopic diseases, and was assessed using the Medical Condition Module (MCM) of the NHANES. The MCM interview was conducted at the MECs to collect self-reported data on a broad range of health conditions, including asthma.
Workload and non-contact injury incidence in elite football players competing in European leagues
Published in European Journal of Sport Science, 2018
Barthelemy Delecroix, Alan McCall, Brian Dawson, Serge Berthoin, Gregory Dupont
While differences in the RR values indicate that workload is an injury risk factor in elite football players, other results indicate that workload does not allow for accurate injury prediction. A risk factor is described by the World Health Organisation as any attribute, characteristic or exposure of an individual that increases the likelihood of developing a disease or injury. The predictive ability of a factor is assessed by its accuracy in predicting the occurrence (or not) of an event. Lu, Howle, Waterson, Duncan, and Duffield (2017) found that the sRPE derived training load sustained 3 weeks, 2 weeks and 1 week before an injury was associated with injury incidence in professional football players. However, when the authors clustered the group to analyse the predictive ability of the sRPE, they found a sensitivity of only 16.3% and a very low difference (1.9%) between the negative and positive predictive values. These results (Lu et al., 2017) are in accordance with the present study, indicating that sRPE is a useful injury risk factor, but not an injury prediction tool.