Epilepsy Detected Using a New Method Based on Volumetric Analysis Results from Brain MR Images

Epilepsy is a challenging brain disease that requires significant clinical findings. (1) Background: The aim of this study is to improve the success rate of epilepsy detection using a newly developed method by optimizing the high-dimensional dataset obtained from brain MRI images. Standard machine learning models fall short of achieving the desired success in high-dimensional datasets. To achieve this, we aimed to develop an optimized hybrid model by combining the local classification power of the k-Nearest Neighbor classifier and the anomaly detection success of the negative selection algorithm. (2) Methods: Cortical and subcortical brain regions were analyzed to examine volumetric differences. A dataset was created by identifying regions statistically significant for epilepsy. This dataset was then optimized using the Scatter Search Snake Optimization algorithm. The performances of six different machine learning models trained on this optimized dataset were compared. (3) Results: The standard and popular models, SVM (82.70%), kNN (78.70%), RF (69.30%), MLP (73.30%), and NSA (95.89%), demonstrated a detection success rate. In contrast, the proposed hybrid model, kNN-NSA (98.65%), demonstrated a detection success rate. (4) Conclusions: The optimized hybrid kNN-NSA approach, which considers local density in such high-dimensional datasets and tolerates outliers within the self-data, appears to outperform traditional methods. Furthermore, this study has demonstrated that volumetric differences in regions not previously reported in the literature, such as WM-hypointensities, ventral DC, and choroid plexus, may be effective in the decision-making process for diagnosing epilepsy, as they are also found to be significant.