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Developing a Decision on the Type of Prostate Cancer Using FAHP
Published in Ali Emrouznejad, William Ho, Fuzzy Analytic Hierarchy Process, 2017
There are at least three types of prostate cancer that are known thus far. The American Society of Clinical Oncology noted that adenocarcinoma is the most common type of prostate cancer, accounting for more than 95% of prostate cancer cases. Adenocarcinoma is a cancer that begins in the glandular tissue of the prostate cancer. Glands are structures that secrete fluids; in the case of the prostate gland, the gland cells secrete prostate fluid that combines with seminal fluid during ejaculation. The American Cancer Society explains that most adenocarcinomas grow slowly. In fact, many autopsies of men who died from causes other than prostate cancer actually had prostatic adenocarcinoma without their knowledge (Oncology, 2009). Adenocarcinomas of the prostate are characterized by the development of dense blastic metastases, whereas small-cell carcinomas of the prostate typically produce lytic bone metastases (Sozen et al., 2000). Sarcoma is another type of prostate cancer. An international health-care center in the United States reports that prostatic sarcoma is a rare form of prostate cancer, making up less than 0.1% of all prostate cancers. This type of prostate cancer primarily affects men who are in the age range of 35–60 years. The tumor in prostatic sarcoma often grows very large, causing obstruction in the flow of urine from the bladder out through the urethra. It may also increase the urge to urinate at night, also called nocturia. This type of cancer arises from cells that have the potential of developing into muscles, lymphatic vessels, blood, and connective tissue (Galil Medical, 2007).
Effects of sleep deprivation on perceived and performance fatigability in females: An exploratory study
Published in European Journal of Sport Science, 2022
Justine R. Magnuson, Hogun J. Kang, Mathew I. B. Debenham, Chris J. McNeil, Brian H. Dalton
Sleep deprivation (SD) affects individuals with medical or neurological conditions (nocturia, pain, etc.) and, due to work and life commitments, also healthy individuals (e.g. medical professionals, shift workers, parents, etc.) (Murphy & Delanty, 2007). Despite the high prevalence of SD, little is known about its effects on perceived and performance fatigability. Perceived fatigability refers to changes in sensations that regulate the integrity of the performer, whereas performance fatigability is a decline in objective measures of performance (Enoka & Duchateau, 2016). Both attributes influence motor function and may be negatively impacted by SD (Pilcher & Huffcutt, 1996; Temesi et al., 2013; Van Helder & Radoki, 1989), which would bring to light important health, safety, and performance concerns for sleep-deprived individuals. Further, perception of fatigability and/or effort in the context of a physically-fatiguing task might be markedly different from performance capacity (i.e. performance fatigability), which is an important consideration given that workplace and daily-life activities are typically carried out based on subjective perceptions of effort and fatigue (Sagherian & Geiger Brown, 2016; Yazdi & Sadeghniiat-Haghighi, 2015).
Prostate cancer classification with MRI using Taylor-Bird Squirrel Optimization based Deep Recurrent Neural Network
Published in The Imaging Science Journal, 2022
Goddumarri Vijay Kumar, Mohammed Ismail Bellary, Thota Bhaskara Reddy
Prostate cancer is a harmful infection that influences the reproductive system of men [1,2]. The second most frequent kind of cancer in men and the fifth most common cancer-related death worldwide is prostate cancer [2–5]. However, most prostate cancer forms slowly and moves from the prostate portion to some other area of human parts specifically in lymph nodes, whereas the other cancer glands grow faster. However, for men, 99% of cases of prostate cancer start around age 50 [2,6–9]. A 2012 survey conducted in 84 nations found that over 1.1 million men have prostate cancer, and of those, 307,000 will die from the fluid that is produced. Because prostate cancer has no symptoms and only a few signs, such as haematuria, nocturia, constant urination, femur, urine stream, bone pain, fluid discharge, etc., it is very difficult to identify prostate cancer at an early stage [2,10,11]. Prostate cancer can be predicted using the antigen testing mechanism, which monitors the disease and strives to detect cancer, but this mechanism is very complex to decide in a shorter time that further increases the mortality factor and the overall risk [2,12]. To overcome the traditional testing procedure, the automatic system is introduced to reduce the risk factors during the prediction of prostate cancer [2,13].
Introduce structural equation modelling to machine learning problems for building an explainable and persuasive model
Published in SICE Journal of Control, Measurement, and System Integration, 2021
Jiarui Li, Tetsuo Sawaragi, Yukio Horiguchi
As shown in Figure 8, the factor loading of Nocturia to the underlying disease is lower than 0.3, which is not favourable. After removing the Nocturia variable from the model, Table 3 shows the final factor loadings.