مقاله انگلیسی رایگان در مورد رویکرد فازی عصبی برای تشخیص افسردگی پس از زایمان – اسپرینگر ۲۰۱۸

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مشخصات مقاله
ترجمه عنوان مقاله یک رویکرد فازی عصبی برای تشخیص اختلال افسردگی پس از زایمان
عنوان انگلیسی مقاله A neuro fuzzy approach for the diagnosis of postpartum depression disorder
انتشار مقاله سال ۲۰۱۸
تعداد صفحات مقاله انگلیسی ۹ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه اسپرینگر
نوع نگارش مقاله
مقاله پژوهشی (Research article)
مقاله بیس این مقاله بیس نمیباشد
فرمت مقاله انگلیسی  PDF
رشته های مرتبط روانشناسی، مهندسی کامپیوتر
گرایش های مرتبط روانشناسی بالینی، هوش مصنوعی
نوع ارائه مقاله
ژورنال
مجله / کنفرانس مجله علوم کامپیوتر ایران – Iran Journal of Computer Science
دانشگاه Department of Computer Science – University of Benin – Benin City – Nigeria
کلمات کلیدی افسردگی، بعد از زایمان، فازی عصبی، تشخیص
کلمات کلیدی انگلیسی Depression, Postpartum, Neuro fuzzy, Diagnosis
شناسه دیجیتال – doi
https://doi.org/10.1007/s42044-018-0021-6
کد محصول E9318
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فهرست مطالب مقاله:
Abstract
۱ Introduction
۲ Review of related works
۳ Materials and methods
۴ Results
۵ Discussion
References

 

بخشی از متن مقاله:

Introduction

Medicine is a broad field with mental health being one of its major branches. A major problem in mental health involves the diagnosis of illness which is based on clinical symptoms. Depression is a common mental illness worldwide, with more than 300 million people affected [1]. It can negatively affect a person’s feeling, thinking, behavior, ability to function. Symptoms of depressions include feeling sad, lack of interest in games or activities previously enjoyed, weight loss, insomnia, fatigue, feeling worthless, suicidal thought and extreme difficulty in thinking or making decisions [2–۴]. Depending on the number and severity of symptoms, a depressive episode can be categorized as mild, moderate or severe. Statistics have shown that depression affects persons across the ages of 15 and 74 with the ages between 25 and 40 having a larger percentage [1]. Nursing mothers are not left out of the category of individuals that can also experience depression. A type of depression experienced after childbirth is called postpartum depression (PPD) and it is a serious mental health condition that affects an estimated 13–۱۹% of nursing mothers [5]. Some investiga tors have reported a PPD prevalence of 10–۱۵% in developed world and about 22% in developing countries. Cases of up to 35% and above have also been reported in the literature [6, 7]. Postpartum depression is characterized as a persistent low mood in new mothers, which is often accompanied by the symptoms of depression [3, 8]. Postpartum depression differs from the postpartum blue which is a briefer period of emotional disturbance that is experienced among some women within the first few days after childbirth and usually disappear within 10 days [9]. The pathogenesis of PPD is still unclear. However, the role of the hormonal fluctuations in the postpartum period may not be ruled out, with emphasis on the rapid decrease in progesterone, estradiol, and estriol [10]. Also the activity of the hypothalamic–pituitary–thyroid axis and thyroid dysfunction is being canvassed [11]. The risk factors of postpartum depression are usually previous incidence of depression or anxiety during pregnancy, stressful recent life events, poor social support, childcare stress, low self-esteem, and difficult infant temperament. Others may include single marital status, poor relationship with partner, and lower socioeconomic status including income. Incidence of PPD had no relationship with maternal age, parity, gender of child, level of education or ethnicity [6, 7, 12]. Maternal PPD interferes with the affection between mother and child, and therefore, impedes the development of the child [13]. Such impediments include negative effects on cognitive development and social–emo tional development of the child [14]. The family may be froth with vices such as child abuse and neglect, marital violence, divorce, etc. [15]. Diagnosis of PPD is based on clinical symptoms and most psychiatrists often fail to recognize it. The Adaptive Neuro Fuzzy Inference System (ANFIS) has proven to be a very power tool in medical diagnosis [16, 17]. Literatures have shown that ANFIS-based intelligence systems for diagnosing medical illnesses have yielded excellent results for some mental health-related conditions [17, 18]. ANFIS combines both neural network and fuzzy logic. The technique applied by ANFIS is quite simple. The fuzzy logic component maps each parameter in the dataset to linguistic labels for that parameter using a membership function. This is used to keep track of input data to output data while the neural network component performs computational analysis on the dataset.

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