Reliable Parkinson’s Disease Detection by Analyzing Handwritten Drawings: Construction of an Unbiased Cascaded Learning System Based on Feature Selection and Adaptive Boosting Model

Ali, Liaqat; Zhu, Changan; Golilarz, Noorbakhsh Amiri; Javeed, Ashir; Zhou, Mingyi; Liu, Yipeng

doi:10.1109/access.2019.2932037

Cited by 86 publications

(47 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e first component of the system is a feature selection module, while the second component is a predictive model. Feature selection methods use data mining concepts to improve the performance of the machine learning models [27,28]. e feature selection module uses a search strategy to find out the optimal subset of features which are applied to the DNN that acts as a predictive model.…”

Section: E Proposed Methodmentioning

confidence: 99%

Heart Risk Failure Prediction Using a Novel Feature Selection Method for Feature Refinement and Neural Network for Classification

Javeed

Rizvi

Zhou

et al. 2020

Mobile Information Systems

Self Cite

View full text Add to dashboard Cite

Diagnosis of heart disease is a difficult job, and researchers have designed various intelligent diagnostic systems for improved heart disease diagnosis. However, low heart disease prediction accuracy is still a problem in these systems. For better heart risk prediction accuracy, we propose a feature selection method that uses a floating window with adaptive size for feature elimination (FWAFE). After the feature elimination, two kinds of classification frameworks are utilized, i.e., artificial neural network (ANN) and deep neural network (DNN). Thus, two types of hybrid diagnostic systems are proposed in this paper, i.e., FWAFE-ANN and FWAFE-DNN. Experiments are performed to assess the effectiveness of the proposed methods on a dataset collected from Cleveland online heart disease database. The strength of the proposed methods is appraised against accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and receiver operating characteristics (ROC) curve. Experimental outcomes confirm that the proposed models outperformed eighteen other proposed methods in the past, which attained accuracies in the range of 50.00–91.83%. Moreover, the performance of the proposed models is impressive as compared with that of the other state-of-the-art machine learning techniques for heart disease diagnosis. Furthermore, the proposed systems can help the physicians to make accurate decisions while diagnosing heart disease.

show abstract

Section: E Proposed Methodmentioning

confidence: 99%

Heart Risk Failure Prediction Using a Novel Feature Selection Method for Feature Refinement and Neural Network for Classification

Javeed

Rizvi

Zhou

et al. 2020

Mobile Information Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…Weight = 0.891 $ + 0.076 ) + ⋯ − 0.07 $ † + $ ⋯ (13) For one thing, from a horizontal viewpoint, Table II contains the linear combination relationship of 29 independent variables and FACT1, ⋯, FACT16, as shown in formula (14).…”

Section: Dimensionality Reduction Results Of Acute Liver Failure mentioning

confidence: 99%

“…In general, dimensionality reduction methods include Chi2, MI, LDA, PCA, Factor Analysis(FA) and autoencoder dimensionality reduction, and so on. While processing the sound signal data, Ali et al used linear discriminant analysis (LDA) and Chi2 statistical model (Chi2) rank to detect automatic Parkinson's disease (PD) [12][13][14]. Based on Particle Swarm Optimization (PSO) for feature selection, Souriet al established a fault prediction model in Internet of Things Applications [15].…”

mentioning

confidence: 99%

The Comparison of LightGBM and XGBoost Coupling Factor Analysis and Prediagnosis of Acute Liver Failure

Zhang

Gong

2020

IEEE Access

View full text Add to dashboard Cite

“…For the imbalanced class problem, since the correct prediction of the majority classes will overwhelm that of the minority classes, the prediction performance should not be evaluated based on the average accuracy [24]. For example, the predictions of the majority are non-informatively high if one simply predicts all samples pertaining to the majority class.…”

Section: Comparison Results On Original Datamentioning

confidence: 99%

Explanation and Prediction of Clinical Data with Imbalanced Class Distribution based on Pattern Discovery and Disentanglement

Zhou

Wong

2020

Preprint

View full text Add to dashboard Cite

Background: Statistical data analysis, especially the advanced machine learning (ML) methods, have attracted considerable interest in clinical practices. We are looking for interpretability of the diagnostic/prognostic results that will bring confidence to doctors, patients and their relatives in therapeutics and clinical practice. When datasets are imbalanced in diagnostic categories, we notice that the ordinary ML methods might produce results overwhelmed by the majority classes diminishing prediction accuracy. Hence, we need methods that could produce explicit transparent and interpretable results in decision-making, without sacrificing accuracy, even for data with imbalanced groups. Methods: In order to interpret the clinical patterns and conduct diagnostic prediction of patients with high accuracy, we develop a novel method, Pattern Discovery and Disentanglement for Clinical Data Analysis (cPDD), which is able to discover patterns (correlated traits/indicants) and use them to classify clinical data even if the class distribution is imbalanced. In the most general setting, a relational dataset is a large table such that each column represents an attribute (trait/indicant), and each row contains a set of attribute values (AVs) of an entity (patient). Compared to the existing pattern discovery approaches, cPDD can discover a small succinct set of statistically significant high-order patterns from clinical data for interpreting and predicting the disease class of the patients even with groups small and rare.Results: Experiments on synthetic and thoracic clinical dataset showed that cPDD can 1) discover fewer patterns compared to other existing pattern discovery methods; 2) allow the users to interpret succinct sets of patterns coming from uncorrelated sources, even the groups are rare/small; and 3) obtain better performance in prediction compared to other interpretable classification approaches. Conclusions: In conclusion, cPDD discovers fewer patterns with greater comprehensive coverage to improve the interpretability of patterns discovered. Experimental results on synthetic data validated that cPDD discovers all patterns implanted in the data, displays them precisely and succinctly with statistical support for interpretation and prediction, a capability which the traditional ML methods lack. The success of cPDD as a novel interpretable method in solving the imbalanced class problem shows its great potential to clinical data analysis for years to come.

show abstract

Reliable Parkinson’s Disease Detection by Analyzing Handwritten Drawings: Construction of an Unbiased Cascaded Learning System Based on Feature Selection and Adaptive Boosting Model

Cited by 86 publications

References 26 publications

Heart Risk Failure Prediction Using a Novel Feature Selection Method for Feature Refinement and Neural Network for Classification

Heart Risk Failure Prediction Using a Novel Feature Selection Method for Feature Refinement and Neural Network for Classification

The Comparison of LightGBM and XGBoost Coupling Factor Analysis and Prediagnosis of Acute Liver Failure

Explanation and Prediction of Clinical Data with Imbalanced Class Distribution based on Pattern Discovery and Disentanglement

Contact Info

Product

Resources

About