Data classification using rough set and bioinspired computing in healthcare applications - an extensive review

Kumari, Nancy; Acharjya, D. P.

doi:10.1007/s11042-022-13776-1

Cited by 14 publications

(5 citation statements)

References 96 publications

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“…Firstly, the centering and normalization operation is performed by subtracting the mean of the signals and normalizing the amplitude between [−1, 1]. Using a linear-phase Kaiser window, we apply the band pass filter between [2,46] Hz to eliminate the noise interference. It has been confirmed by several of our experiments that this frequency range can include fetal cardiac activities.…”

Section: Signal Pre-processingmentioning

confidence: 99%

“…For FHR estimation, the window length is therefore adequate to assess the FECG signal quality. In addition, the STFT, with a window length of 128 ms and a step size of 5 ms, is applied to generate a 2D spectrogram of each signal segment, and the spectrogram is extracted at a frequency of [2,60] Hz. By selecting the most appropriate parameters for spectrogram generation, we can make sure that the spectrograms include enough information on fetal cardiac activity for subsequent signal quality analysis [36].…”

Section: Signal Pre-processingmentioning

confidence: 99%

“…Since there are not many suitable resources, tools, or skills accessible for medical data analysis, many machine learning and deep learning techniques are developed for several medical research topics recently [2], including heart rate estimation [3], disease diagnosis [4], blood pressure estimation [5], patient activity recognition [6], biomedical signal quality assessment [7], etc. The two primary task categories in machine learning are supervised and unsupervised learning.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised Learning-Based Non-Invasive Fetal ECG Muti-Level Signal Quality Assessment

et al. 2023

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Objective: To monitor fetal health and growth, fetal heart rate is a critical indicator. The non-invasive fetal electrocardiogram is a widely employed measurement for fetal heart rate estimation, which is extracted from the electrodes placed on the surface of the maternal abdomen. The qualities of the fetal ECG recordings, however, are frequently affected by the noises from various interference sources. In general, the fetal heart rate estimates are unreliable when low-quality fetal ECG signals are used for fetal heart rate estimation, which makes accurate fetal heart rate estimation a challenging task. So, the signal quality assessment for the fetal ECG records is an essential step before fetal heart rate estimation. In other words, some low-quality fetal ECG signal segments are supposed to be detected and removed by utilizing signal quality assessment, so as to improve the accuracy of fetal heart rate estimation. A few supervised learning-based fetal ECG signal quality assessment approaches have been introduced and shown to accurately classify high- and low-quality fetal ECG signal segments, but large fetal ECG datasets with quality annotation are required in these methods. Yet, the labeled fetal ECG datasets are limited. Proposed methods: An unsupervised learning-based multi-level fetal ECG signal quality assessment approach is proposed in this paper for identifying three levels of fetal ECG signal quality. We extracted some features associated with signal quality, including entropy-based features, statistical features, and ECG signal quality indices. Additionally, an autoencoder-based feature is calculated, which is related to the reconstruction error of the spectrograms generated from fetal ECG signal segments. The high-, medium-, and low-quality fetal ECG signal segments are classified by inputting these features into a self-organizing map. Main results: The experimental results showed that our proposal achieved a weighted average F1-score of 90% in three-level fetal ECG signal quality classification. Moreover, with the acceptable removal of detected low-quality signal segments, the errors of fetal heart rate estimation were reduced to a certain extent.

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Section: Signal Pre-processingmentioning

confidence: 99%

Section: Signal Pre-processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Unsupervised Learning-Based Non-Invasive Fetal ECG Muti-Level Signal Quality Assessment

et al. 2023

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“…Extracting meaningful knowledge from hybrid data, which encompasses both categorical and numerical data, presents a significant challenge. Two predominant strategies have emerged to address this challenge 9 , 10 . The first strategy involves employing numerical data processing techniques such as Principal Component Analysis (PCA) 11 , 12 , Neural Networks 13 – 16 , and SVM 17 .…”

Section: Introductionmentioning

confidence: 99%

Adaptive neighborhood rough set model for hybrid data processing: a case study on Parkinson’s disease behavioral analysis

Raza,

Jamal,

Qureshi

et al. 2024

Sci Rep

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Extracting knowledge from hybrid data, comprising both categorical and numerical data, poses significant challenges due to the inherent difficulty in preserving information and practical meanings during the conversion process. To address this challenge, hybrid data processing methods, combining complementary rough sets, have emerged as a promising approach for handling uncertainty. However, selecting an appropriate model and effectively utilizing it in data mining requires a thorough qualitative and quantitative comparison of existing hybrid data processing models. This research aims to contribute to the analysis of hybrid data processing models based on neighborhood rough sets by investigating the inherent relationships among these models. We propose a generic neighborhood rough set-based hybrid model specifically designed for processing hybrid data, thereby enhancing the efficacy of the data mining process without resorting to discretization and avoiding information loss or practical meaning degradation in datasets. The proposed scheme dynamically adapts the threshold value for the neighborhood approximation space according to the characteristics of the given datasets, ensuring optimal performance without sacrificing accuracy. To evaluate the effectiveness of the proposed scheme, we develop a testbed tailored for Parkinson’s patients, a domain where hybrid data processing is particularly relevant. The experimental results demonstrate that the proposed scheme consistently outperforms existing schemes in adaptively handling both numerical and categorical data, achieving an impressive accuracy of 95% on the Parkinson’s dataset. Overall, this research contributes to advancing hybrid data processing techniques by providing a robust and adaptive solution that addresses the challenges associated with handling hybrid data, particularly in the context of Parkinson’s disease analysis.

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“…AI-based diagnosis methods have seen particular attention in medical and healthcare systems as they can often become a life-saving tool for people [34][35][36][37][38][39][40]. For example, since data from Alzheimer's Disease (AD) are multimodal and time series in nature, it is a burden for specialists to handle all the data that their patients provide; thus, they may not be able to correctly decide on the outcomes [38].…”

Section: Introductionmentioning

confidence: 99%

NSSI questionnaires revisited: A data mining approach to shorten the NSSI questionnaires

Farajzadeh

Sadeghzadeh

2023

PLoS ONE

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Background and objective Non-suicidal self-injury (NSSI) is a psychological disorder that the sufferer consciously damages their body tissues, often too severe that requires intensive care medicine. As some individuals hide their NSSI behaviors, other people can only identify them if they catch them while injuring, or via dedicated questionnaires. However, questionnaires are long and tedious to answer, thus the answers might be inconsistent. Hence, in this study for the first time, we abstracted a larger questionnaire (of 662 items in total) to own only 22 items (questions) via data mining techniques. Then, we trained several machine learning algorithms to classify individuals based on their answers into two classes. Methods Data from 277 previously-questioned participants is used in several data mining methods to select features (questions) that highly represent NSSI, then 245 different people were asked to participate in an online test to validate those features via machine learning methods. Results The highest accuracy and F1 score of the selected features–via the Genetics algorithm–are 80.0% and 74.8% respectively for a Random Forest algorithm. Cronbach’s alpha of the online test (validation on the selected features) is 0.82. Moreover, results suggest that an MLP can classify participants into two classes of NSSI Positive and NSSI Negative with 83.6% accuracy and 83.7% F1-score based on the answers to only 22 questions. Conclusion While previously psychologists used many combined questionnaires to see whether someone is involved in NSSI, via various data mining methods, the present study showed that only 22 questions are enough to predict if someone is involved or not. Then different machine learning algorithms were utilized to classify participants based on their NSSI behaviors, among which, an MLP with 10 hidden layers had the best performance.

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Data classification using rough set and bioinspired computing in healthcare applications - an extensive review

Cited by 14 publications

References 96 publications

Unsupervised Learning-Based Non-Invasive Fetal ECG Muti-Level Signal Quality Assessment

Unsupervised Learning-Based Non-Invasive Fetal ECG Muti-Level Signal Quality Assessment

Adaptive neighborhood rough set model for hybrid data processing: a case study on Parkinson’s disease behavioral analysis

NSSI questionnaires revisited: A data mining approach to shorten the NSSI questionnaires

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