Feature Selection with Artificial Bee Colony Algorithms for Classifying Parkinson’s Diseases

摘要: Parkinson’s is a brain disease that affects the quality of human life significantly with very slow progresses. It known early diagnosis great importance to arrange relevant and efficient treatments. Data analytics particularly predictive approaches such as machine learning techniques can be efficiently used for earlier diagonosis. As typical big data problem, number features in collected symptoms per case matters crucially. higher considered more complexities incur handling algorithms. This leads dimensionality problem datasets, which requires optimisation overcome trade-off between complexity accuracy. In this study, artificial bee colony-based feature selection methods are employed order select most prominent successful Disease classification over datasets. The optimised set were training testing k nearest neigbourhood algorithm, then verifed support vector algorithm public dataset. study demonstrates binary versions colony algorithms significanlty comparison literature.