TOPLULUK YAKLAŞIMINA DAYALI MAKİNE ÖĞRENME TEKNİKLERİ KULLANARAK ERKEN DÖNEM DİYABET RİSK TAHMİNİ

Year 2024, Volume: 13 Issue: 2, 74 - 85, 30.07.2024

Tuğba Palabaş

https://doi.org/10.18036/estubtdc.1320922

Abstract

En ölümcül hastalıklardan biri olarak kabul edilen Diabetes Mellitus yaygın görülen kronik bir hastalıktır. Aynı zamanda başta nöropati, nefropati ve retinopati olmak üzere birçok hastalığın ortaya çıkmasına neden olur. Bu bağlamda semptomların doğru değerlendirilerek hastalığın erken teşhisi ve hızlı bir tedavi sürecinin başlatılması çok önemlidir. Bu çalışmanın amacı, diyabet riskini erken evrede en iyi doğrulukla belirleyebilecek etkili bir model sunmaktır. Bunun için diyabet risk tahmininde sıklıkla kullanılan sınıflandırma algoritmaları topluluk yaklaşımları ile desteklenmektedir. İlk olarak, Naive Bayes (NB), Trees-J48, k En Yakın Komşu (kNN) ve Sıralı Minimal Optimizasyon (SMO) sınıflandırıcılarının performansı, Sylhet'teki Sylhet Diyabet Hastanesi hastalarından doğrudan anketlerle toplanan 520 örneklik bir veri seti kullanılarak ayrı ayrı analiz edilmiştir. , Bangladeş. Daha sonra Adabost, Bagging ve Random Sub-Space (RSS) algoritmalarının sınıflandırıcı başarısı üzerindeki etkileri incelenmiş ve Adabost yaklaşımına dayalı j48 sınıflandırıcının bu veri setinde en iyi doğruluğa sahip olduğu gösterilmiştir. Son olarak, diyabetin tahmin maliyetini azaltmak ve sınıflandırma başarısını artırmak için Wrapper Subset Eval (WSE) öznitelik çıkarım algoritması uygulanmaktadır. Böylece önerilen sınıflandırıcı yöntemi ile indirgenmiş veri seti kullanılarak %99 ile en iyi doğruluk elde edilmiştir.

Keywords

Diyabet, Sınıflandırma, Topluluk Yaklaşımı, Özellik Çıkarma

EARLY-STAGE DIABETES RISK PREDICTION USING MACHINE LEARNING TECHNIQUES BASED ON ENSEMBLE APPROACH

Abstract

Diabetes Mellitus which is considered as one of the deadliest is a common, chronic disease. It also causes the emergence of many diseases, especially neuropathy, nephropathy and retinopathy. In this context, early diagnosis of the disease by accurately evaluating the symptoms and initiating a rapid treatment process is very important. The aim of this study is to present an effective model that can determine the diabetes risk in eary-stage with the best accuracy. To do so, the classification algorithms that are frequently used in diabetes risk estimation are supported with ensemble approaches. Firstly, the performance of Naive Bayes (NB), Trees-J48, k Nearest Neighbor (kNN) and Sequential Minimal Optimization (SMO) classifiers is analyzed separately by using a dataset of 520 samples collected with direct questionnaires from Sylhet Diabetes Hospital patients in Sylhet, Bangladesh. Then, the effects of Adabost, Bagging and Random Sub-Space (RSS) algorithms on classifier success are investigated and it is shown that the j48 classifier based on Adabost approach has the best accuracy in this dataset. Finally, the Wrapper Subset Eval (WSE) feature extraction algorithm is applied to reduce the estimation cost of diabetes and increase classification success. Thus, the best accuracy at 99% is achieved using reduced data set with proposed classifier method.

Keywords

Diabetes, Classification, Ensemble Approach, Feature Extraction

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