An extensive experimental evaluation of automated machine learning methods for recommending classification algorithms

This paper presents an experimental comparison among four automated machine learning (AutoML) methods for recommending the best classification algorithm for a given input dataset. Three of these methods are based on evolutionary algorithms (EAs), and the other is Auto-WEKA, a well-known AutoML method based on the combined algorithm selection and hyper-parameter optimisation (CASH) approach. The EA-based methods build classification algorithms from a single machine learning paradigm: either decision-tree induction, rule induction, or Bayesian network classification. Auto-WEKA combines algorithm selection and hyper-parameter optimisation to recommend classification algorithms from multiple paradigms. We performed controlled experiments where these four AutoML methods were given the same runtime limit for different values of this limit. In general, the difference in predictive accuracy of the three best AutoML methods was not statistically significant. However, the EA evolving decision-tree induction algorithms has the advantage of producing algorithms that generate interpretable classification models and that are more scalable to large datasets, by comparison with many algorithms from other learning paradigms that can be recommended by Auto-WEKA. We also observed that Auto-WEKA has shown meta-overfitting, a form of overfitting at the meta-learning level, rather than at the base-learning level

Evaluation of the frequency of non-motor symptoms of Parkinson’s disease in adult patients with Gaucher disease type 1

Abstract Background Gaucher disease (GD) is caused by deficiency of beta-glucocerebrosidase (GCase) due to biallelic variations in the GBA1 gene. Parkinson’s disease (PD) is the second most common neurodegenerative condition. The classic motor symptoms of PD may be preceded by many non-motor symptoms (NMS), which include hyposmia, rapid eye movement (REM) sleep behavior disorder, constipation, cognitive impairment, and depression. Population studies have identified mutations in GBA1 as the main risk factor for idiopathic PD. The present study sought to evaluate the prevalence of NMS in a cohort of patients with GD type 1 from Southern Brazil. Methodology This is an observational, cross-sectional study, with a convenience sampling strategy. Cognition was evaluated by the Montreal Cognitive assessment (MoCa), daytime sleepiness by the Epworth Scale, depression by the Beck Inventory, constipation by the Unified Multiple System Atrophy Rating Scale, and REM sleep behavior disorder by the Single-Question Screen; hyposmia by the Sniffin’ Sticks. Motor symptoms were assessed with part III of the Unified Parkinson’s Disease Rating Scale. All patients were also genotyped for the GBA1 3′-UTR SNP (rs708606). Results Twenty-three patients (female = 13; on enzyme replacement therapy = 21, substrate reduction therapy = 2) with a mean age of 41.45 ± 15.3 years (range, 22–67) were included. Eight patients were found to be heterozygous for the 3′-UTR SNP (rs708606). Fourteen patients (8 over age 40 years) presented at least one NMS; daytime sleepiness was the most frequent (n = 10). Two patients (aged 63 and 64, respectively) also presented motor symptoms, probably drug-related. Conclusions NMS were prevalent in this cohort. We highlight the importance of a multidisciplinary follow-up focusing on earlier diagnosis of PD, especially for patients with GD type 1 over the age of 40