Perinatal dönemdeki psikiyatrik hastalık belirtilerinin yapay zeka tabanlı büyük veri işleme platformu ile belirlenmesi
Detection of psychiatric disease symptoms in the perinatal period with an ai-based big data processing platform
Yazar: NUR BANU OĞUR
Danışman: PROF. DR. CELAL ÇEKEN
Sakarya Üniversitesi / Fen Bilimleri Enstitüsü / Bilgisayar Mühendisliği Ana Bilim Dalı
Derece: Doktora, 2023
Konu: Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol = Computer Engineering and Computer Science and Control

Özet:
Makine öğrenimi, bilim ve endüstrinin yönlendirici güçlerinden biridir; ancak büyük
verinin oluşmasıyla birlikte, bu muazzam veri hacmini işleyebilmek için geleneksel
makine öğrenimi algoritmalarında değişiklik yapma ihtiyacı ortaya çıkmaktadır. Bu
veri setlerinin boyutu ve karmaşıklığı, geleneksel veri işleme yöntemleri yardımıyla
ele alınmasını zor bir hale getirmiştir. İşte burada büyük veri analitiği teknolojileri
devreye girmektedir. Büyük veri analitiği teknolojileri sayesinde, yüksek hacimli
sağlık veri kümelerinin mevcudiyeti olması durumunda dahi, istenilen hızda ve
performansta çeşitli tıbbi durumlar doğru bir şekilde teşhis edilebilmektedir.
Büyük veri analitiği teknolojileri, veri setlerinin içindeki değerli bilgileri ortaya
çıkarmak için çeşitli analitik yöntemler kullanmaktadır. İstatistiksel analiz, veri
madenciliği, makine öğrenimi ve yapay zeka gibi teknikler, bu büyük veri setlerinden
anlamlı sonuçlar elde etmeyi sağlamaktadır. Bunların yanında, büyük veri setlerini
paralel ve dağıtık bir şekilde işleyerek hızlı analizler yapılmasını da sağlamaktadır.
Büyük veri analitiği teknolojilerinin sağladığı bu yetenekler, birçok alanda büyük bir
etki yaratmıştır. Sağlık alanı da bu alanlardan biridir.
Çalışmanın birincil amacı, performansı optimize edilmiş algoritmalar aracılığıyla
hamile kadınlarda anksiyete ve depresyonla ilgili en zorlayıcı soruları belirlemektir.
Bu sayede daha az soru ile daha kısa sürede sonuca ulaşmak amaçlanmaktadır. Bu
çalışmanın bir sonraki hedefi, büyük veri akışında makine öğrenimi modellerini
kullanarak hamile kadınlarda depresyon ve anksiyete için anlık bir uzaktan sağlık
durumu tahmin sistemi oluşturmaktır. Bu hedef, Apache Spark büyük veri işleme
motorunu temel almaktadır. Bu ölçeklenebilir sistemde uygulama, hastanın sağlık
durumunu tahmin etmek için gebe kadınlardan veri almaktadır. Bu veriler işlenebilir
hale getirilebilmesi adına ön işlem süreçlerinden geçirilmektedir. Performans adına
yapılan bir dizi işlemden sonra bu veri kümesi için en iyi sonuçları üreten algoritmanın,
Naïve Bayes makine öğrenimi algoritması olduğu tespit edilmiştir.
Çalışmada ayrıca, anlık olarak sonuç üreten tahmin sistemi, Kubernetes kümeleme
sistemine entegre edilmiştir. Böylece, oluşturulan ölçeklenebilir mimarinin işlem
süresi açısından araştırılması sağlanmıştır. Geliştirilen büyük veri platformunun
başarımı dikkate alındığında, hamile kadınlarda anksiyete ve depresyonu tespit etmek
için kullanılan geleneksel yöntemler yerine, gerçek zamanlı olarak önemli doğruluk
oranında ve hızda çalışan bilgisayar tabanlı sistemlerin kullanılabileceğini söylemek
mümkündür.

Abstract:
One of the forces shaping driving science and industry is machine learning. This
technology, which enables computers to learn from data instead of complex algorithms
written by humans, has revolutionized many industries.
Machine learning is a tool used to discover patterns and relationships on large data
sets. This enables businesses to better understand their data, uncover confidential
information and predict future trends. Machine learning also plays a large role in
automated decision-making processes. In many areas, tasks that are difficult or 
time consuming for humans to do can be delegated to machines. For these reasons, machine
learning is an increasingly important technology today. However, the prevalence of
big data requires new uses for this large amount of data, which has different speeds
than traditional approaches to applying machine learning techniques.
Big data is a valuable concept that plays an important role in machine learning. Today,
the rapidly increasing amount of data across various industries enables businesses to
make better decisions, make predictions and better understand customer needs.
Technologies used in big data analytics are needed to process this large amount of data
under the necessary conditions.
Big data analytics technologies play an indispensable role today to process and analyze
large data sets and extract valuable information. Big data analytics tools enable
businesses to deal with complex data masses, discover patterns and make better
decisions. One of its key advantages is the data processing capacity of big data
analytics tools. Large datasets are created quickly, often in high volume. Processing
and analyzing this data may not be possible with traditional methods. Big data
analytics tools enable rapid analysis by processing these large data sets in a parallel
and distributed manner. In addition, it offers data visualization and reporting
capabilities. Because large data sets are often complex and voluminous, it is important
to make sense of and share data. Big data analytics technology provides better
understanding of data by visualizing it with graphs, tables and interactive visuals. It
also provides users with the ability to create shareable reports that summarize data with
its reporting features. In summary, big data analytics technologies are important
technologies that help businesses effectively manage, analyze and extract valuable
information from large data sets. These technologies facilitate the processing and
interpretation of big data with features such as data processing capacity, analysis
capabilities, visualization and reporting features, forecasting and forecasting
capabilities. There are many areas covered by big data, and one of the most critical
areas is medicine, which requires rapid decision-making. Psychiatry, on the other
hand, has an important place in the field of medicine as a branch focusing on mental
health. Psychiatry, which examines mental, emotional and behavioral disorders, evaluates
people's mental health, makes a diagnosis and applies appropriate treatment methods.
Mental health diseases are health problems that deeply affect the lives of individuals
and need to be treated carefully. If psychological disorders such as anxiety and
depression occurring in the perinatal period, which includes the pregnancy process,
are not detected on time, they will have negative effects on both the mother and the
baby. The effects on the perinatal period and pregnant women are of great importance
for the mental health of the society. For this reason, rapid diagnosis process is of great
importance in psychiatry. Early diagnosis of mental health problems enables early
initiation of appropriate treatment and better outcomes for patients.
Although anxiety and depression, which are common in the perinatal period, are
diseases known by physicians and whose negative consequences are better understood
day by day, they are not sufficiently recognized, and even if recognized, adequate
treatment is not applied. Anxiety and depression in the perinatal period need to be
diagnosed and treated early, as it causes serious harm to the society. It is possible to
solve this need with big data analytics systems.
With big data analytics, it is possible to quickly process pregnant data and transform
it into meaningful information. This can enable doctors, healthcare professionals and
researchers to provide fast and accurate diagnoses, treatment plans and healthcare
services.
In our study, a big data analytics system, which can produce instant results in the
diagnosis of anxiety and depression in women in the perinatal period, and which
integrates scalable architecture thanks to its cluster structure, has been built. While
creating this system, technologies such as Apache Kafka, Apache Spark, Kubernetes
were used. Thus, it is aimed to minimize the potential harm caused by depression and
anxiety in women in the perinatal period.
The primary aim of this study is to identify the most influential questions on outcome
related to anxiety and depression in pregnant women by extracting features through
performance-optimized algorithms. In this way, it is aimed to reach the result in a
shorter time with fewer questions. The next goal of this study is to find the machine
learning algorithm that gives the most optimal result on these cleaned data. Its next
goal is to create an instant remote health status prediction module for depression and
anxiety in pregnant women, based on the Apache Spark big data processing engine,
which focuses on using machine learning models in big data flow. Its final goal is to
integrate the modules containing these purposes into the Kubernetes clustering system.
In this study, first of all, the data was made ready for processing by passing through
the preprocessing stages. There are sixty questions that pregnant women need to
answer. The optimized selection-evolutionary feature selection algorithm, which
determined this number as optimum, was reduced to sixteen. These; gestational week,
baby's health problem, living place (urban or rural), communication with her spouse,
education level, number of people living at home, working status, total income level,
emotional support from her husband, presence of people to share her problems with,
exercise status, educational status of her husband, whether there is a desired
pregnancy, the sex of the baby, smoking status, and the chronic illness of the spouse.
Various algorithms (Decision Tree, Naive Bayes, K-Nearest Neighbor (K-NN),
Random Forest, Gradient Boosted Tree (GBT), Logistic Regression and Deep Feed
Forward Neural Network (DFFNN)) has been run on these data to predict the health
status of the pregnant woman. It is concluded that Naïve Bayes machine learning algorithm produces the best results with 90.8% and 81.71% accuracy and precision,
respectively.
All the work done was built on the Kubernetes cluster, and the infrastructure, which
includes a scalable architecture suitable for big data that produces results about the
disease, has been developed. By performing a performance test on this infrastructure,
the effects of the increase in the number of executors on the result were observed. As
a result of the performance tests, it was concluded that the spark executor, which is
effective in parallelization, contributed positively in terms of speed. In addition, the
effect of the increase in the number of executors on resource consumption was also
examined, and CPU and memory resource usages were included in the diagrams.
Considering the performance of the developed big data platform, it is possible to
conclude that computer-based systems operating in real-time with significant accuracy
and speed can be used to detect anxiety and depression in pregnant women, replacing
traditional methods.