Early Detection of Prion Disease Using Genetic Algorithm-Based Feature Selection and Random Forest

Abstract: Prion diseases are rare but invariably fatal disorders affecting the nervous system [8]. Identifying them at an early stage is complicated when working with large-scale omics data, as the datasets often contain few patient samples and many irrelevant or overlapping features [9]. In this work, we employ a genetic algorithm (GA) to perform feature selection, integrated with a Random Forest (RF) classifier for prediction [10]. Experiments on synthetic biomarker datasets, followed by external testing, showed that the GA could isolate concise feature sets that enhanced model generalization [11]. The final configuration reached a hold-out accuracy of at least 0.97 and achieved 0.94 accuracy on an unseen test set [12]. We detail the methodology, performance trends, selected features, and the potential impact on biomarker identification and early clinical diagnostics.[13]

Keywords: Prion Disease; Transmissible Spongiform Encephalopathy; Genetic Algorithm; Feature Selection; Random Forest; Biomarkers [14].