Today, people use web‐based technologies to meet their information needs, socialise, communicate, and deal with formal and informal processes. At the same time, mobile versions of these applications provide people with great convenience in daily life. These applications include blood‐pressure monitors, blood‐glucose monitors, body‐analysis scales, pulse oximeters, and activity and sleep trackers. Many of these products sync directly with a free mobile app that makes monitoring, viewing, storing, and sharing of health vitals simple and comprehensive. The data collected from the user is stored in a cloud‐based application, then trained by intelligent algorithms that use machine learning for health aims so that the user can instantly see his or her status and development. In this study, the aim was to construct a cloud‐based application specific to women for monitoring pregnancy. In the web‐based application working with membership logic, members can access machine learning assisted calculators of the baby percentile, period tracker, pregnancy calendar, and baby vaccination schedule. Moreover, they can access augmented/virtual‐reality‐assisted visual training.
Abstract:The process for obtaining information that will create value on a large-scale data stack is called data mining by its general name. Data mining is commonly used in sales and marketing departments, in determining strategies and making critical decisions for the future in many sectors. Similarly, data mining is used in the determination of health policies, more effective implementation of health services and in the management of resources and institutions in the health sector. In this study, it was aimed to create a software architecture of data mining that will help the personal monitoring of the pregnancy process in a more effective way in the health sector. Many different types of data such as age, gender, location, education, physical characteristics, lifestyle habits and medical history of the people that could be used for this purpose are stored online by health institutions. The machine learning algorithms have been created to determine classification, clustering and association rule on these data.
Aim: It is known that adverse experiences in childhood are associated with various mental and physical illnesses. In some studies, it is stated that it also affects women's reproductive health. The aim of this study is to determine the relationship between childhood adverse experiences and the prevalence of premenstrual syndrome. Material and Method: The research was conducted as a cross-sectional and relationship seeker at a public university on young girls aged between 18 and 25 years. The sample size was calculated to be at least 623 students with 0.05 error level, 80% representation power and 99% confidence interval, and the study was conducted with 754 participants. To collect data; "Personal Introduction Form", "Childhood Adverse Experiences Scale (ACES)" and "Premenstrual Syndrome Scale (PMSÖ)" were used. In statistical evaluation; arithmetic mean, percentage distribution, standard deviation, linear regression analysis were used. Results: 60.5% of the students had at least one ACE, the average age was 20.68±1.98, 52% studied at the faculty of health sciences, 40.3% studied in the first year, 73.2% had PMS, the average age of menarche was found to be 14.84±28.82. In addition, the mean score of ACES is 1.50±1.75, and the average of PMSÖ is 132.36±36.22. As a result of the linear regression analysis, it has been determined that ACES affects the total and all sub-dimensions of PMSÖ. Conclusion:In conclusion, it can be said that adverse childhood experiences affect PMS symptoms and PMS symptoms increase as the number of ACES increases.
Amaç: Bu araştırmada, çocukluk çağındaki olumsuz yaşantılar ve postpartum depresyon düzeyi ile emzirme öz yeterliliği arasındaki ilişkinin Yapısal Eşitlik Modeli ile incelenmesi amaçlanmıştır. Yöntem: Tanımlayıcı-Kesitsel nitelikte yapılan araştırmanın evrenini Türkiye’nin doğusunda bulunan bir kamu hastanesinde doğum yapan lohusalar oluşturmuştur. Power analizi yaptığımızda örneklem büyüklüğü %90 güven aralığı %95 evreni temsil gücüyle en az 250 lohusa olarak hesaplanmış ve araştırma gönüllü 266 lohusa ile tamamlanmıştır. Veriler, “Kişisel Tanıtım Formu”, “Çocukluk Çağı Olumsuz Yaşantılar Ölçeği”, “Edinburgh Doğum Sonrası Depresyon Ölçeği” ve “Emzirme Öz Yeterlilik Ölçeği” ile toplanmıştır. İstatistiksel değerlendirmede; tanımlayıcı istatistiklerin (sayı, yüzde, ortalama, standart sapma, min-max) yanı sıra, Cronbach’s alfa, açıklayıcı faktör analizi, pearson korelasyon analizi ve Yapısal Eşitlik Modeli kullanılmıştır. Bulgular: Bu araştırmada katılımcıların %49.2’sinin en az bir tane çocukluk çağında olumsuz deneyimler yaşadığı, çocukluk çağı olumsuz yaşantılar ölçeğinden aldıkları toplam puan ortalamasının 1,05±1.50, Edinburgh Postpartum Depresyon Ölçeğinden aldıkları toplam puan ortalamasının 6.05±6.01, emzirme öz yeterliliği ölçeğinden aldıkları toplam puan ortalamasının ise 57.8±10.6 olduğu belirlendi. Çocukluk Çağı Olumsuz Yaşantılar Ölçeği ile Edinburgh Postpartum Depresyon Ölçeği arasında pozitif ve Edinburgh Postpartum Depresyon Ölçeği ile Emzirme Öz Yeterlilik Ölçeği arasında negatif yönde istatistiksel olarak anlamlı bir ilişki saptandı (r=.250*; p=.000; ve r=-.303*; p=.000). Yapısal eşitlik modeline göre; Çocukluk Çağı Olumsuz Yaşantılar ve Edinburg Postpartum Depresyon Ölçeklerinden alınan puanların Emzirme Öz Yeterlilik Ölçeğinden alınan puanın %10’unu açıkladığı belirlendi. Sonuçlar ve Öneriler: Emzirme öz yeterliliğinin çocukluk çağı olumsuz yaşantılar ve postpartum depresyondan etkilendiği, depresyonun emzirme öz yeterliliği üzerinde daha etkili bir değişken olduğu saptandı. Ayrıca çocukluk çağı olumsuz yaşantıların postpartum depresyon üzerinde önemli bir etkisi olduğu belirlendi.
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