SOSYAL HİZMETLERDE VERİ TEMELLİ MÜDAHALE OLANAKLARI: EVDE BAKIM HİZMETİ ALAN BİREYLERİN K-MEANS ALGORİTMASI İLE KÜMELENMESİ

Year 2025, Volume: 34 Issue: Uygarlığın Dönüşümü - Sosyal Bilimlerin Bakışıyla Yapay Zekâ, 417 - 437, 20.07.2025

Yasin Erdurak

Abstract

Yaşlanan nüfus, kronik hastalıkların artışı ve yalnız yaşayan bireylerin çoğalması evde bakım hizmetlerine duyulan ihtiyacı her geçen gün artırmaktadır. Literatürde evde bakım hizmetlerinden yararlanan bireylerin bireysel özelliklerine göre farklı ihtiyaçlar sergilediği bilinmekle birlikte bu farklılıkların sistematik olarak analizine yönelik veri temelli çalışmalar sınırlıdır. Bu bağlamda mevcut araştırma evde bakım hizmeti alan bireylerin demografik, sosyoekonomik ve hizmet kullanım özelliklerine göre benzer örüntüler sergileyip sergilemediğini belirlemeyi ve bu yolla hedefe yönelik sosyal hizmet müdahaleleri geliştirilmesine katkı sunmayı amaçlamaktadır. Araştırmada İzmir Büyükşehir Belediyesi tarafından sağlanan ve 2020-2024 yıllarını kapsayan “Evde Bakım Hizmeti Talepleri” veri seti kullanılmıştır. Analiz aşamasından önce eksik, tutarsız ve anlam belirsizliği taşıyan kayıtlar veri setinden çıkarılmış ve analizler toplam 5245 geçerli gözlem üzerinden gerçekleştirilmiştir. Veri hazırlığı sürecinde kategorik değişkenler one-hot kodlama yöntemiyle sayısal formata dönüştürülmüş, ardından farklı ölçeklerdeki tüm değişkenler StandardScaler yöntemiyle standardize edilmiştir. Araştırmada kullanılan yöntemlerden olan K-Means kümeleme algoritmasının uygulanmasında optimum küme sayısı Elbow ve Silhouette yöntemleriyle belirlenmiş ve analiz için en uygun küme sayısının beş olduğuna karar verilmiştir. Elde edilen kümeler, Temel Bileşen Analizi (PCA) yardımıyla görselleştirilmiş ve kümelerin özellikleri ortalama Z-skorları üzerinden yorumlanmıştır. Analiz bulguları bireylerin yaş, cinsiyet, fiziksel yeterlilik, sosyal güvence durumu ve hizmet talepleri gibi değişkenler temelinde anlamlı şekilde farklılaşan kümeler oluşturduğunu ortaya koymaktadır.

Keywords

K-Means algoritması, Denetimsiz makine öğrenmesi, Veri analizi, Evde bakım hizmeti, Sosyal hizmet

Ethical Statement

Yapılan çalışma için etik kurul onayı gerekmemektedir. İnternette yayınlanmış veri seti kullanılarak araştırma yapılmıştır.

DATA-BASED INTERVENTION OPPORTUNITIES IN SOCIAL SERVICES: CLUSTERING HOME CARE SERVICE RECIPIENTS USING THE K-MEANS ALGORITHM

Abstract

The aging population, increase in chronic diseases, and rise in the number of individuals living alone have all contributed to the escalating demand for home care services. While it is known that individuals who benefit from home care services exhibit different needs according to their individual characteristics, there is a limited number of data-driven studies that systematically analyze these differences. In this context, the current study aims to determine whether individuals receiving home care services exhibit similar patterns based on their demographic, socioeconomic, and service usage characteristics, thereby contributing to the development of targeted social work interventions. The study used the “Home Care Service Requests” dataset provided by the Izmir Metropolitan Municipality, covering the years 2020 to 2024. Before the analysis phase, records that were incomplete, inconsistent, or ambiguous were removed from the dataset, and the analyses were performed on a total of 5,245 valid observations. During the data preparation process, categorical variables were converted to numerical format using the one-hot encoding method, and then all variables with different scales were standardized using the StandardScaler method. In the application of the K-Means clustering algorithm, one of the methods used in the study, the optimal number of clusters was determined using the Elbow and Silhouette methods, and it was decided that the most appropriate cluster count for the analysis was five. The obtained clusters were visualized using Principal Component Analysis (PCA), and the characteristics of the clusters were interpreted based on average Z-scores. The analysis findings reveal that the clusters differ significantly based on variables such as age, gender, physical capacity, social security status, and type of service requests.

Keywords

K-Means algorithm, Unsupervised machine learning, Data analysis, Home care, Social work

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