Prediction of posttraumatic epilepsy using machine learning

Post-traumatic Epilepsy is one of the common aftereffects of brain injury. This neurological disorder can persist throughout the lifetime of patients and impacts their quality of life significantly. Identification of markers that indicate the likelihood of developing PTE can help develop preventive care for subjects identified as at risk. Despite the relatively high prevalence of PTE, brain imaging-based biomarkers for the diagnosis of PTE are lacking. This is due in part to the heterogeneity of injury in traumatic brain injury patients. Here we investigate the use of structural and functional imaging features for training machine learning models. Recently, applying state of the art machine learning methods such as neural networks on clinical data to help diagnosis attract a considerable amount of attention. However, the choice of a good algorithm and subset of features is fundamental to achieve reliable classification. Our goal is to explore if different Machine Learning techniques could be leveraged for the early diagnosis of PTE. We compared four popular machine learning methods performance to predict PTE after brain injury (1) support vector machine and (2) random forest (3) fully connected neural network and (4) graph convolutional network. Our result demonstrates the advantage of using a combination of connectivity features (functional) and lesion volume (structural) in conjunction with a Kernel SVM approach in predicting PTE. We also shown using a feature reduction method such as principal component analysis (PCA) is more effective than penalizing the classifiers. This might be due to the limitation of penalized models for a framework where features are correlatedPost-traumatic Epilepsy is one of the common aftereffects of brain injury. This neurological disorder can persist throughout the lifetime of patients and impacts their quality of life significantly. Identification of markers that indicate the likelihood of developing PTE can help develop preventive care for subjects identified as at risk. Despite the relatively high prevalence of PTE, brain imaging-based biomarkers for the diagnosis of PTE are lacking. This is due in part to the heterogeneity of injury in traumatic brain injury patients. Here we investigate the use of structural and functional imaging features for training machine learning models. Recently, applying state of the art machine learning methods such as neural networks on clinical data to help diagnosis attract a considerable amount of attention. However, the choice of a good algorithm and subset of features is fundamental to achieve reliable classification. Our goal is to explore if different Machine Learning techniques could be leveraged for the early diagnosis of PTE. We compared four popular machine learning methods performance to predict PTE after brain injury (1) support vector machine and (2) random forest (3) fully connected neural network and (4) graph convolutional network. Our result demonstrates the advantage of using a combination of connectivity features (functional) and lesion volume (structural) in conjunction with a Kernel SVM approach in predicting PTE. We also shown using a feature reduction method such as principal component analysis (PCA) is more effective than penalizing the classifiers. This might be due to the limitation of penalized models for a framework where features are correlated.