GLCM Texture Feature Selection for Alzheimer's Detection: A Combination of Statistical Tests, Decision Tree, and Random Forest - Cindyawati, Cindyawati; Suryantari, Risti; Sulungbudi, Janto V. (Universitas Dharma Wacana, 2025)
Alzheimer’s disease is a progressive neurodegenerative disorder that leads to a gradual decline in memory and cognitive function, most commonly affecting individuals over the age of 65. Early detection is essential to enable timely interventions, slow disease progression, and improve quality of life. This study aimed to identify the most dominant texture features from brain MRI images using the Gray Level Co-occurrence Matrix (GLCM) for feature extraction. The extracted features were analyzed through non-parametric statistical tests and machine learning algorithms, including Decision Tree and Random Forest, and validated with cross-validation procedures to ensure robustness. The findings revealed that contrast at 90° consistently emerged as the most significant feature, capturing vertical texture variations associated with brain atrophy, while correlation at 135° provided additional discriminatory power by representing disrupted pixel intensity relationships. In combination, these features enhanced the accuracy of classification models, outperforming other GLCM parameters. The results emphasize that careful selection of texture features improves both accuracy and stability in distinguishing between Alzheimer’s and non-Alzheimer’s brains. This study demonstrates that image-based machine learning frameworks can serve as reliable tools to support early detection of Alzheimer’s disease, offering valuable implications for clinical practice and guiding future research on efficient, non-invasive diagnostic approaches.