EARLY DETECTION OF ALZHEIMER’S DISEASE USING DATA MINING: COMPARISON OF ENSEMBLE FEATURE SELECTION APPROACHES

Abstract

Early Alzheimer's disease detection has become an important research area for many years. Various studies in the field of Alzheimer's disease detection have focused on applying individual feature selection methods. In addition to individual feature selection methods, the ensemble feature selection approach has become a creative field. It advocates the combination of the ranked features from various feature selection methods to obtain better results than the current approaches. Thus, this study aims to build a predictive model for early diagnosis of Alzheimer's disease using the ensemble feature selection approaches. Also, Alzheimer's disease dataset consists of three target classes: Normal (CN), Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD). In this study, homogeneous and heterogeneous ensemble approaches have been applied in the feature selection process. Two feature subsets are created based on these ensemble feature selection approaches. A predictive model for early diagnosis of Alzheimer's disease has been build applying Random Forest, Artificial Neural Network, Logistic Regression, Support Vector Machine, and Naïve Bayes data mining algorithms. The predictive model uses the two feature subsets applying these algorithms separately. Then, the performance results are compared to determine which ensemble feature selection approach performs better than the other. This study revealed that better performance result is provided applying Random Forest algorithm with feature subset obtained using the heterogeneous ensemble feature selection approach (91%).

Keywords

• Alzheimer’s disease prediction
• heterogeneous
• random forest
• data mining
• early diagnosis

Veri Madenciliği Kullanılarak Alzheimer Hastalığının Erken Tespiti: Topluluk Özellik Seçim Yaklaşımlarının Karşılaştırılması

Abstract

Erken Alzheimer hastalığı tespiti uzun yıllardır önemli bir araştırma alanı haline gelmiştir. Alzheimer hastalığı tespiti alanında yapılan çeşitli çalışmalar, bireysel özellik seçme yöntemlerini uygulamaya odaklanmıştır. Bireysel özellik seçme yöntemlerine ek olarak, topluluk özellik seçme yaklaşımı yaratıcı bir alan haline gelmiştir. Bu yaklaşım, mevcut yaklaşımlardan daha iyi sonuçlar elde etmek için çeşitli özellik seçim yöntemlerinden sıralanan özelliklerin kombinasyonunu savunur. Bu nedenle, bu çalışmanın amacı, topluluk özellik seçim yaklaşımlarını kullanarak Alzheimer hastalığının erken teşhisi için bir öngörücü model oluşturmaktır. Ayrıca, Alzheimer hastalığı veri seti üç hedef sınıftan oluşur: Normal (CN), Hafif Bilişsel Bozukluk (MCI) ve Alzheimer hastalığı (AD). Bu çalışmada, özellik seçim sürecinde homojen ve heterojen topluluk yaklaşımları uygulanmıştır. Bu topluluk özellik seçim yaklaşımlarına dayanarak iki özellik alt kümesi oluşturulmuştur. Rastgele Orman, Yapay Sinir Ağı, Lojistik Regresyon, Destek Vektör Makinesi ve Naïve Bayes veri madenciliği algoritmaları uygulanarak Alzheimer hastalığının erken teşhisi için bir tahmin modeli oluşturulmuştur. Bu tahmin modeli yukarıda bahsedilen algoritmaları her iki özellik alt kümesini de ayrı ayrı kullanarak bir tahminde bulunmuştur. Ardından, hangi topluluk özellik seçim yaklaşımının diğerinden daha iyi performans gösterdiğini belirlemek için performans sonuçları karşılaştırılmıştır. Bu çalışma, heterojen topluluk özellik seçim yaklaşımı kullanılarak elde edilen özellik altkümesi ile Rastgele Orman algoritması uygulanarak daha iyi performans sonucunun sağlandığını ortaya koymuştur (% 91).

Keywords

• Alzheimer hastalığı tahmini
• Heterojen
• Rastgele orman
• Veri madenciliği
• Erken tanı

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