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Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, accounting for nearly 60% of all dementia cases. The occurrence of the disease has been increasing rapidly in recent years. Presently about 46.8 million individuals suffer from AD worldwide. The current absence of effective treatment to reverse or stop AD progression highlights the importance of disease prevention and early diagnosis. This research work finds that image feature extraction such as simple RGB Histogram Filter techniques on Alzheimer’s images dataset by implementing statistical learning. The Decision tree – J48 Classifier category gives 0.346 of kappa statistic value, 0.26 of mean absolute error, 0.396 of root mean squared error, 70,88% of relative absolute error, 96.72% of root relative squared error, 0.485 of F-Measure value, 0.344 of Matthews correlation coefficient (MCC)value which is produced an optimal result based on their performance compare with other models.