<![CDATA[Medical image-based disease diagnosis is a complex process and requires a high level of expertise. This study aims to develop an Automatic Diagnosis Assistant using a combination of image feature extraction techniques and Random Forest (RF) classification algorithms. Medical images are processed to extract meaningful textural features, such as using the Gray Level Co-occurrence Matrix (GLCM), which is then used to train the RF model. To address the problem of data imbalance that is common in medical datasets, the SMOTE technique is applied. The performance of the model is evaluated and optimized using Randomized Search to find the best hyperparameters. The results showed that the optimized RF model was able to achieve high accuracy, with significant improvements in the Recall and F1-Score metrics compared to the baseline model. This automated diagnostic assistant is expected to be an effective tool for medical personnel in speeding up and improving diagnostic accuracy, especially in cases with high image volumes.]]>
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