A Hybrid Feature Selection Approach for Parkinson’s Detection Based on Mutual Information Gain and Recursive Feature Elimination

Lamba, Rohit; Gulati, Tarun; Jain, Anurag

doi:10.1007/s13369-021-06544-0

Cited by 21 publications

(16 citation statements)

References 27 publications

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“…The included papers were assessed for cumulative redundancy bias during data extraction. The assessment showed that 8 of the included papers were published by 4 different author groups, 2 papers from Tuncer et al [ 40 , 41 ], 2 papers from Gunduz et al [ 42 , 43 ], 2 papers from Lamba et al [ 44 , 45 ] in the PD group, and 2 papers from Tena et al [ 46 , 47 ] in the amyotrophic lateral sclerosis (ALS) group. To reduce the risk of bias, only 4 of those 8 papers, one with the highest accuracy from each author, were included in the synthesis [ 40 , 42 , 44 , 46 ].…”

Section: Resultsmentioning

confidence: 99%

“…In the diagnosis group, 138 studies were identified. A total of 125 studies in the diagnostic group investigated ML methods to detect a pathological voice, where the participants were grouped as the healthy control (HC) group, with being “healthy” defined as people without a diagnosed disorder, and a group with known pathology [ 17 , 40 , 42 , 44 , 48 - 168 ]. The main idea was to deploy an ML technique for distinguishing those 2 groups from each other with high accuracy.…”

Section: Resultsmentioning

confidence: 99%

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Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review

Idrisoglu¹,

Dallora²,

Anderberg³

et al. 2023

J Med Internet Res

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Background Normal voice production depends on the synchronized cooperation of multiple physiological systems, which makes the voice sensitive to changes. Any systematic, neurological, and aerodigestive distortion is prone to affect voice production through reduced cognitive, pulmonary, and muscular functionality. This sensitivity inspired using voice as a biomarker to examine disorders that affect the voice. Technological improvements and emerging machine learning (ML) technologies have enabled possibilities of extracting digital vocal features from the voice for automated diagnosis and monitoring systems. Objective This study aims to summarize a comprehensive view of research on voice-affecting disorders that uses ML techniques for diagnosis and monitoring through voice samples where systematic conditions, nonlaryngeal aerodigestive disorders, and neurological disorders are specifically of interest. Methods This systematic literature review (SLR) investigated the state of the art of voice-based diagnostic and monitoring systems with ML technologies, targeting voice-affecting disorders without direct relation to the voice box from the point of view of applied health technology. Through a comprehensive search string, studies published from 2012 to 2022 from the databases Scopus, PubMed, and Web of Science were scanned and collected for assessment. To minimize bias, retrieval of the relevant references in other studies in the field was ensured, and 2 authors assessed the collected studies. Low-quality studies were removed through a quality assessment and relevant data were extracted through summary tables for analysis. The articles were checked for similarities between author groups to prevent cumulative redundancy bias during the screening process, where only 1 article was included from the same author group. Results In the analysis of the 145 included studies, support vector machines were the most utilized ML technique (51/145, 35.2%), with the most studied disease being Parkinson disease (PD; reported in 87/145, 60%, studies). After 2017, 16 additional voice-affecting disorders were examined, in contrast to the 3 investigated previously. Furthermore, an upsurge in the use of artificial neural network–based architectures was observed after 2017. Almost half of the included studies were published in last 2 years (2021 and 2022). A broad interest from many countries was observed. Notably, nearly one-half (n=75) of the studies relied on 10 distinct data sets, and 11/145 (7.6%) used demographic data as an input for ML models. Conclusions This SLR revealed considerable interest across multiple countries in using ML techniques for diagnosing and monitoring voice-affecting disorders, with PD being the most studied disorder. However, the review identified several gaps, including limited and unbalanced data set usage in studies, and a focus on diagnostic test rather than disorder-specific monitoring. Despite the limitations of being constrained by only peer-reviewed publications written in English, the SLR provides valuable insights into the current state of research on ML-based voice-affecting disorder diagnosis and monitoring and highlighting areas to address in future research.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review

Idrisoglu¹,

Dallora²,

Anderberg³

et al. 2023

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“… 183 A hybrid MI gain and recursive feature elimination method selected 40 features out of 754 speech features, and it achieves the highest accuracy in classification than other standard feature selection approaches. 184 Features selected by filter and embedded methods were inputted to a wrapper-based approach optimizing both stability and predictability for AD prediction. 185 …”

Section: Resultsmentioning

confidence: 99%

Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review

et al. 2023

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Objective Neurodegenerative diseases affect millions of families around the world, while various wearable sensors and corresponding data analysis can be of great support for clinical diagnosis and health assessment. This systematic review aims to provide a comprehensive overview of the existing research that uses wearable sensors and features for the diagnosis of neurodegenerative diseases. Methods A systematic review was conducted of studies published between 2015 and 2022 in major scientific databases such as Web of Science, Google Scholar, PubMed, and Scopes. The obtained studies were analyzed and organized into the process of diagnosis: wearable sensors, feature extraction, and feature selection. Results The search led to 171 eligible studies included in this overview. Wearable sensors such as force sensors, inertial sensors, electromyography, electroencephalography, acoustic sensors, optical fiber sensors, and global positioning systems were employed to monitor and diagnose neurodegenerative diseases. Various features including physical features, statistical features, nonlinear features, and features from the network can be extracted from these wearable sensors, and the alteration of features toward neurodegenerative diseases was illustrated. Moreover, different kinds of feature selection methods such as filter, wrapper, and embedded methods help to find the distinctive indicator of the diseases and benefit to a better diagnosis performance. Conclusions This systematic review enables a comprehensive understanding of wearable sensors and features for the diagnosis of neurodegenerative diseases.

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“…The main advantage of RFE is that it considers the interactions among features, allowing for selecting of feature subsets that collectively contribute to better predictive performance. However, it can be computationally expensive for large datasets since it requires training the estimator multiple times [ 24 , 25 ]. RFE is a flexible technique that can be applied with various machine-learning algorithms and has proven effective in reducing overfitting, improving model interpretability, and enhancing prediction accuracy by focusing on the most informative features [ 26 ].…”

Section: Methodsmentioning

confidence: 99%

Estimation of Obesity Levels through the Proposed Predictive Approach Based on Physical Activity and Nutritional Habits

Gozukara Bag,

Yagin,

Gormez

et al. 2023

Diagnostics

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Obesity is the excessive accumulation of adipose tissue in the body that leads to health risks. The study aimed to classify obesity levels using a tree-based machine-learning approach considering physical activity and nutritional habits. Methods: The current study employed an observational design, collecting data from a public dataset via a web-based survey to assess eating habits and physical activity levels. The data included gender, age, height, weight, family history of being overweight, dietary patterns, physical activity frequency, and more. Data preprocessing involved addressing class imbalance using Synthetic Minority Over-sampling TEchnique-Nominal Continuous (SMOTE-NC) and feature selection using Recursive Feature Elimination (RFE). Three classification algorithms (logistic regression (LR), random forest (RF), and Extreme Gradient Boosting (XGBoost)) were used for obesity level prediction, and Bayesian optimization was employed for hyperparameter tuning. The performance of different models was evaluated using metrics such as accuracy, recall, precision, F1-score, area under the curve (AUC), and precision–recall curve. The LR model showed the best performance across most metrics, followed by RF and XGBoost. Feature selection improved the performance of LR and RF models, while XGBoost’s performance was mixed. The study contributes to the understanding of obesity classification using machine-learning techniques based on physical activity and nutritional habits. The LR model demonstrated the most robust performance, and feature selection was shown to enhance model efficiency. The findings underscore the importance of considering both physical activity and nutritional habits in addressing the obesity epidemic.

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A Hybrid Feature Selection Approach for Parkinson’s Detection Based on Mutual Information Gain and Recursive Feature Elimination

Cited by 21 publications

References 27 publications

Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review

Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review

Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review

Estimation of Obesity Levels through the Proposed Predictive Approach Based on Physical Activity and Nutritional Habits

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