The ForEx++ based decision tree ensemble approach for robust detection of Parkinson’s disease

Pramanik, Moumita; Pradhan, Ratika; Nandy, Partha; Bhoi, Akash Kumar; Barsocchi, Paolo

doi:10.1007/s12652-022-03719-x

Cited by 7 publications

(5 citation statements)

References 75 publications

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“…Vowel: 59 [ 17 , 40 , 42 , 44 , 48 , 50 , 54 – 59 , 61 – 64 , 68 , 69 , 74 – 85 , 87 – 91 , 93 – 97 , 99 , 100 , 102 , 119 – 122 , 124 , 126 – 128 , 130 – 132 , 135 , 170 , 183 , 184 , 192 ]…”

Section: Resultsunclassified

“…Combined: 45 [ 42 , 44 , 48 , 51 , 52 , 54 , 57 , 62 , 63 , 67 – 70 , 72 , 74 , 76 , 79 , 81 , 85 , 86 , 89 – 91 , 93 , 94 , 96 , 97 , 99 , 100 , 119 , 120 , 122 , 124 , 126 – 131 , 134 , 135 , 170 , 171 , 184 , 192 ]…”

Section: Resultsunclassified

“…UCI g : 33 [ 40 , 42 , 44 , 50 , 53 , 55 – 57 , 60 , 61 , 63 , 64 , 68 , 69 , 71 , 82 , 84 , 91 , 93 , 99 , 100 , 119 , 120 , 122 , 124 , 126 , 127 , 129 , 133 , 135 , 183 , 188 , 192 ]…”

Section: Resultsunclassified

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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: Resultsunclassified

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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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“…Ma & Zhang [17] conducted a study titled "A decision tree-based approach to software defect prediction: A case study in the automotive industry." This study aimed to evaluate the effectiveness of decision tree-based approaches in predicting software defects in the automotive industry.…”

Section: Shukla and Guptamentioning

confidence: 99%

Software Defect Prediction System Based on Decision Tree Algorithm

Chinenye,

Anyachebelu,

Abdullahi

2023

AJRCoS

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Software defect prediction plays a crucial role in ensuring software quality and minimizing the potential risks associated with defects. This study aims to develop a comprehensive software defect prediction system that utilizes tree-based algorithms to enhance accuracy, feature selection, and evaluation metrics. The study addresses the limitations of previous research by considering a broader range of datasets, comparing computational efficiency with other ensemble techniques, and examining the impact of hyperparameters on model performance. The implemented system consists of three stages: dataset loading, processing, and result presentation. The dataset loading page allows users to upload their datasets in CSV format, simplifying the prediction process. The processing page performs essential tasks such as feature engineering, normalization using minimax normalization, and training the model with the decision tree algorithm. These steps ensure the extraction of relevant features, transformation of data, and learning of patterns and correlations for accurate software defect prediction. The study emphasizes the practical implementation of the developed system, going beyond model evaluation. By providing a fully functional and integrated system, this study bridges the gap between research and real-world application. The findings of this study contribute to the field of software defect prediction by offering an improved system that enhances accuracy, feature selection, and evaluation metrics. This has implications for software development and quality assurance processes, ultimately leading to higher software quality and increased productivity.

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“…The e.g. [2] proposed four different PD detection decision tree integration methods based on the ForEx++ rule framework. In this paper, SysFor and ForestPA decision forest algorithms are used to control the number of trees in forest during training, which has significant detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

KNN Normalized Optimization and Platform Tuning Based on Hadoop

Chi

2022

IEEE Access

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Big data has become part of the life for many people. The data about people's life are being continously collected, analysized and applied as our society progresses into the big data era. Behind the scene, the computer server clusters need to process hundres of millions pieces of data every day. It is very important to choose the right big data processing platform and algorithm to deal with different kinds of datasets. Therefore, in order to be fully familiar with the related work of driving big data processing, it is necessary to master the classification algorithm of data. It aims to help us carry out a classification model or operation analysis of classification function by screening and classifying the current data in data mining. In addition, the given data can be mapped to the specified category area, and the development trend of future data can be predicted through classification models. So this kind of algorithm helps to reduce the difficulty of work operation and improve people's work efficiency. This paper optimizes the classical classification algorithm-KNN, and designs a new normalized algorithm called PEWM_G KNN. From the perspective of distance measurement, we use Pearson correlation coefficient to replace the traditional Euclidean Metric, then we further refine the study for attribute values of datasets and introduce the entropy weight method, combined with Pearson's measure to optimize the distance calculation equation. After the K value is fixed, we added Gaussian Function to carry out the selection of classification. In this study, we compared the effects of every step, and tested datasets with different data types and sizes, in order to test the performance of the algorithm under different scenarios. The datasets we used include Iris, Breast Cancer, Dry Bean and HTRU2 (All datasets are from The University of California, Irvine). Finally, we further analyze the performance of different system configuration parameters on the prediction rate and time. The experimental results show that PEWM_G KNN algorithm has better optimization effect for datasets with more complex attribute values and more records than the original KNN algorithm. Moreover, the optimization of platform parameters improves prediction rates of algorithms and reduces the time. We tested PEWM_G KNN on the Hadoop platform, confiured with HDFS, Hadoop-YARN, ZooKeeper, Hadoop-HA and MapReduce.

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The ForEx++ based decision tree ensemble approach for robust detection of Parkinson’s disease

Cited by 7 publications

References 75 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

Software Defect Prediction System Based on Decision Tree Algorithm

KNN Normalized Optimization and Platform Tuning Based on Hadoop

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