Hastane Aciline Gelenlerde Kalp Krizi Risk Olasılığının Belirlenmesi için Bir Karar Destek Sisteminin Oluşturulması
Establishment of a Decision Support System for Determining the Risk Probability of Heart Attack in Hospital Emergency Visitors
Yazar: Özdemir, Gülüzar; Bilen, Ömer; Canım Ateş, Sezen
Dergi: Duzce University Journal of Science and Technology, Vol. 10 No. 4, 2021
Anahtar kelimeler: Engineering ; Mühendislik ; Kalp krizi,Pearson Ki-Kare,Karar ağacı modelleri,Karar destek sistemleri ; Heart attack,Pearson Chi-Square,Decision tree models,Decision support systems

Bilindiği üzere akut kalp krizinden ölümlerin çoğu, medikal tedaviye başlanmadan gelişse de hastane içi mortalite 50'li yıllarda %30-35 oranında iken günümüzde akut kalp krizinin hastane mortalitesi %5-7 civarına indiği görülmektedir. Bunun başlıca nedeni biyokimyasal belirteç kontrollerinin gelişen teknoloji ile artması ve tanı sistemlerinin gelişmesidir. Günümüzde pek çok hastalığın referans değerlerinin yıllara ya da farklı faktörlere göre değiştiği bilinmektedir. Ayrıca hasta şikâyetleri ile bulguların karşılaştırmalı bir çalışması yapılmadığından yapay zekâ çağına geçilen günümüzde hastaların acil servislerde belirttiği şikâyetlerin standardize edilerek yazılımsal bir program oluşturulmasının acil servis hekimlerine fayda sağlayacağı düşünülmektedir. Bu amaçla bu çalışmada,Bezmialem Vakıf Üniversitesi Tıp Fakültesi Hastanesi Acil Servis birimine farklı şikâyetlerle başvuran hastalarda kalp krizi risk faktörleri istatistiksel olarak değerlendirilmiştir. Mayıs– Ekim 2016 tarihleri arasında 6607 hasta verisinin analizi kan tahlilleri, şikâyet ve muayene bulguları olarak 3 aşamada düzenlenmiştir. Bu veriler SPSS 24.0 paket programıyla ki-kare bağımsızlık testi ve karar ağacı modellemesi ile istatistiksel olarak değerlendirilmiştir. Hastaların ön muayenede belirttiği şikâyetler ile kalp krizi ve cinsiyete göre ki-kare bağımsızlık testi uygulanmıştır. Buna göre; kalp krizi ile göğüs ağrısı, çarpıntı, terleme ve nefes darlığı şikâyetlerinin bağımlı olduğu belirlenirken; cinsiyete göre de karın ağrısı, çarpıntı, sırt ağrısı, baş dönmesi, halsizlik, nefes darlığı, bulantı ve kusma şikâyetlerinin bağımlı olduğu görülmüştür. Karar ağacı modellemesinde ise, modele bağımlı değişken sadece kalp krizi olarak seçilirken bağımsız değişkenler olarak yaş, cinsiyet, kan tahlili sonuçları, hasta şikâyet ve muayene bulguları eklenerek 4 farklı modelde incelenmiştir. Bu incelemelerin sonucunda yaş, çarpıntı, terleme, cinsiyet, göğüs ağrısı, diyastolik arter basıncı ve Troponin I’nın önemli olduğu saptanmıştır. Böylece elde edilen sonuçlar değerlendirildiğinde acile gelenlerde kalp krizi risk olasılığının belirlenmesi için 4 farklı karar destek sistemi oluşturulmuştur. Buna göre hekimler acile gelenlerdeki ilk bulgulara göre kalp krizi geçirme riskini karar destek sistemleri vasıtasıyla belirleyebileceklerdir.

As it is known, although most of the deaths from acute heart attack occur before medical treatment is started, whilein-hospital mortality was 30-35% in the 1950s, it is seen that hospital mortality of acute heart attack has decreasedto around 5-7% today. The main reason for this is the increase in biochemical marker controls with the developing technology and the development of diagnostic systems. Today, it is known that the reference values of manydiseases change according to years or different factors. In addition, since there is not a comparative study of patient complaints and findings, it is thought that the creation of a software program by standardizing the complaints ofpatients in the emergency services in the era of artificial intelligence will benefit emergency physicians. For thispurpose, in this study, heart attack risk factors were statistically evaluated in patients who applied to the EmergencyDepartment of Bezmialem Vakıf University Medical Faculty Hospital with different complaints. The analysis of6607 patient data between May and October 2016 was organized in 3 stages as blood tests, complaints, andexamination findings. The analysis of 6607 patient data between May and October 2016 was organized in 3 stagesas blood tests, complaints, and examination findings. These data were statistically evaluated by chi squareindependence test and decision tree modeling with SPSS 24.0 package program. Chi- square independence test was applied according to the complaints of the patients in the preliminary examination, heart attack and gender. According to this, it was determined that heart attack and chest pain, palpitation, sweating and shortness of breath were dependent; It was observed that the complaints of abdominal pain, palpitation, back pain, dizziness, weakness, shortness of breath, nausea and vomiting were dependent on gender. In the decision tree modeling examined in 4 different models, while the dependent variable was selected as the only heart attack; the independent variables were added as age, gender, blood test results, patient complaints and medical examination findings. As a result of these examinations, age, palpitation, sweating, gender, chest pain, diastolic arterial pressure and Troponin I were found to be important. Thus, when the obtained results were evaluated, 4 different decision support systems were created to determine the risk of heart attack in those who came to the emergency services. Accordingly, physicians will be able to determine the risk of having a heart attack by means of decision support systems according to the first findings of those who come to the emergency services.