China 
Africa 
Explore chapters and articles related to this topic
Performance Comparison of Different Machine Learning Techniques towards Prevalence of Cardiovascular Diseases (CVDs)
Published in Om Prakash Jena, Bharat Bhushan, Nitin Rakesh, Parma Nand Astya, Yousef Farhaoui, Machine Learning and Deep Learning in Efficacy Improvement of Healthcare Systems, 2022
In the human body, the heart is one of the main body organs and plays an important role in a human body like the brain; it circulates blood to each of the body parts. However, the circulating system is so important because it carries oxygen, blood, or other materials to the different organs of the body [1,2]. For the last couple of years, CVDs or heart disease have become the most prominent problem in medical science, and the number of people suffering from this problem is increasing day by day. The well-known or common types of heart diseases are hypertensive heart disease, CVDs, pulmonary stenosis, heart murmurs, and heart failure, etc. [3,4]. Every year, approximately 17 million people died of CVDs, specifically strokes and heart attacks reported by the World Health Organization (WHO) [5]. In this way, identifying necessary symptoms and health habits contribute towards CVDs or create heart problems. Table 9.1 shows some of the most common symptoms or factors of heart disease.
Chronic Arsenic Exposure to Drinking Water
Published in M. Manzurul Hassan, Arsenic in Groundwater, 2018
Conducting a cross-sectional study of 405 villagers of Inner Mongolia with blood pressure who had been drinking water with an inorganic arsenic content of <50 μg/L, Zhang et al. (2013) show that the OR for prevalence of abnormal PP and MAP was 1.06 (0.24–4.66) and 0.87 (0.36–2.14) in the group with 30–50 years of exposure and was 2.46 (0.87–6.97) and 3.75 (1.61–8.71) for the group with >50 years exposure, compared to the group with arsenic exposure ≤30 years, respectively, (Zhang et al., 2013). Examining a total of 382 men and 516 women residing in villages from BFD-endemic areas in Taiwan, Chen et al. (1995) proved the prevalence of hypertension as the long-term effect of arseniasis, declaring that “the higher the cumulative long-term arsenic exposure, the higher the prevalence of hypertension” with a 1.5-fold increase in arsenic-exposed residents with age-sex adjusted prevalence of hypertension compared to residents in nonendemic areas. In addition, Mazumder and Dasgupta (2011) suggest a relationship between arsenic exposure and noncirrhotic portal hypertension. Lewis et al. (1999) identified hypertensive heart disease as related to chronic arseniasism.
Pharmaceutical Applications of Collagen
Published in Amit Kumar Nayak, Md Saquib Hasnain, Dilipkumar Pal, Natural Polymers for Pharmaceutical Applications, 2019
K. Sangeetha, A. V. Jisha Kumari, E. Radha, P. N. Sudha
The alteration in phenotype and cross-linking of collagen appears to have considerable impacts on the ventricular remodeling seen in pressure overload hypertrophy. The collagen changes elucidating diastolic dysfunction and chamber remodeling have been shown in animals. They had to extrapolate these findings to late ventricular remodeling that occurs in hypertensive heart disease in humans. The major inciting stimulus for these changes in collagen type and structure is not clearly known. Limited data is available to understand and explain the transformation of cross-linked to non-cross-linked collagen leading to cardiac remodeling.
5GSS: a framework for 5G-secure-smart healthcare monitoring
Published in Connection Science, 2022
Jianqiang Hu, Wei Liang, Osama Hosam, Meng-Yen Hsieh, Xin Su
A patient enters into the smart home environment with a mobile intelligent terminal. For example, in his smart environment, the patient needs to check his ECG level, blood pressure level and environmental parameters to follow his health conditions. Combined with environmental parameters, the situation of “hypertensive heart disease” can be identified by referencing the average electrocardiogram (ECG) level and blood pressure level. When the prototype system detects emergency situations that represent heart rate (“Sinus tachycardia”, RRi<500 ms) and blood pressure (Blood Pressure>160mmHg), his mobile intelligent terminal receives alarm messages from the edge server as quickly as possible. Health situation identification-based SWRL rules-based reasoning