Algorithms for clustering, classification and selection of informative features for early detection of brain cancer

Abstract

Early detection of brain cancer is crucial for improving treatment outcomes and patient survival rates. This study proposes a set of algorithms for clustering, classification, and selection of informative features that can aid in the early diagnosis of brain tumours. In collaboration with medical experts, a dataset comprising 218 patient records and 82 clinical and symptomatic features was constructed. Through clustering analysis, the dataset was grouped into four diagnostic classes: (1) Anaplastic astrocytoma in the right frontal region, (2) Adenoma in the chiasmal-sellar region, (3) Glioblastoma in the right frontal lobe, and (4) Meningioma in the right frontal lobe. A feature selection algorithm was then applied to identify the most diagnostically relevant attributes. From the initial 82 features, 19 were determined to be strongly indicative of the disease classes. Further refinement using the proposed algorithm resulted in a subset of six highly informative features, which successfully differentiated the classes with a minimum object similarity threshold of 65 %. The approach demonstrates the potential of data-driven techniques in enhancing the accuracy and efficiency of brain cancer diagnostics, offering a scalable method that could be integrated into clinical decision-support systems.