Enhancing Diagnostic Accuracy of Neurological Disorders Through Feature-Driven Multi-Class Classification with Machine Learning.

ABSTRACT: Background/Objectives: Neurological disorders (ND) are a global health challenge, affecting millions and greatly reducing quality of life. Disorders such as Alzheimer's disease, mild cognitive impairment (MCI), schizophrenia, and depression often share overlapping symptoms, complicating diagnosis and treatment. Early detection is crucial for timely intervention; however, traditional diagnostic methods rely on subjective assessments and costly imaging, which are not universally accessible. Addressing these challenges, this study investigates the classification of multiple ND using electroencephalography (EEG) signals. Methods: Various feature extraction methods were employed, and the Least Absolute Shrinkage and Selection Operator (Lasso) algorithm was utilized for effective f

SUBMITTER: Altıntop CG 

PROVIDER: S-EPMC12427693 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature