Pushing Constraints by Rule-Driven Pruning Techniques in Non-Uniform Minimum Support for Predicting Obstructive Sleep Apnea

Sim, Doreen Ying Ying; Teh, Chee Siong; Ismail, Ahmad Izuanuddin

doi:10.4028/www.scientific.net/amm.892.210

Cited by 6 publications

(1 citation statement)

References 12 publications

(32 reference statements)

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“…This research adopts visualization techniques using PCA and/or PCVG algorithms embarking on schema enumerated trees (i.e. SETs) after investigating the data features and characteristics in the datasets [1,[3][4][5] before applying AdaBoost ensemble. AdaBoost is one of the most popular ensemble methods to be used for further improving certain weak classifiers such as decision trees [1,4,13,[14][15][16].…”

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confidence: 99%

Pushing Visualization Effects into Pushed Schema Enumerated Tree-Based Support Constraints

Sim

Teh

Ismail³

2019

AMM

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Based on the datasets from UCI and Obstructive Sleep Apnea, a disparate methodology of uncovering the visualization effects into the pushed support constraints of schema enumerated tree-based classification techniques is proposed and presented in this paper. This is to actively ‘wipe out’ the redundant growing effects of decision trees through itemset generation when visualization techniques are applied using Principal Component Analysis (PCA) and/or Principal Component Variable Grouping (PCVG) algorithms. Enumeration specification is based on the schema enumerated tree (SET) drawn after sorting out the features and characteristics on each dataset applied. The linchpin is to streamline the pre-tree classification effects for post-tree classification by using visualization techniques, i.e. PCA and/or PCVG, which are applied during the SET development. The over-fitting effects done during the SET development by the pushed support constraints can be counter-corrected by fewer PCA and/or PCVG imposed during visualization processes. The under-fitting effects done by the imprecise ‘early stopping’ of the SET development can be counter-corrected by greater PCA and/or PCVG imposed during the post-tree classification techniques through pushed SET support constraint learning. Research outcome on all the investigated datasets showed that the prediction accuracies have been profoundly improved after applying visualization of PCA and/or PCVG algorithms into the pushed SET-based or SET-based support constraints.

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confidence: 99%

Pushing Visualization Effects into Pushed Schema Enumerated Tree-Based Support Constraints

Sim

Teh

Ismail³

2019

AMM

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Intellective Site System of University Library on Account of Elevated Apriori Algorithm (AA)

Cao

Yuan

2023

Lecture Notes in Electrical Engineering

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Literature Review on Sleep APNEA Analysis by Machine Learning Algorithms Using ECG Signals

Ankitha

Manimegalai

Jose

et al. 2021

J. Phys.: Conf. Ser.

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Obstructive Sleep Apnea is a respiratory disorder that impairs sleep quality by causing respiratory arrest. An irregular breath delay or decrease of airflow during sleep is the hallmark of the apnea syndrome. According to the literature, approximately 2% of middle-aged women and 4% of middle-aged men are affected. The disease is diagnosed by the physician in two steps. In the first stage, the physician reviews the medical records obtained using the polysomnography system. The disease is diagnosed in two stages by the physician, who examines the patient records taken with the polysomnography system in the first stage. New diagnostic processes and equipment are required as a result of the negative aspects of this procedure. The heart rate variable (HRV) and electrocardiography (ECG) signals are used, and ECG records from the patient and control groups are obtained. The optical filter was used to clean ECG signals and heart rate variables (HRV) from patient and control classes. After that, the ECG signal was used to calculate the HRV parameter. The HRV and ECG signals were then used to extract functionality. Reduced machine learning techniques, such as random forest, SVM, and the kNN feature selection process, were used to classify the extracted features. To evaluate the classifiers’ efficiency, the sensitivity and specificity values, as well as the accuracy rates for each class in the test set, were computed, and a receiver operating characteristic curve was developed. The method can be realized with Random forest, Support Vector Machine, and KNN, which have the best accuracy of 82.5 percent, 97 percent, and 89 percent, respectively, using 11 ECG and HRV features, according to the results. The system will work with these success rates. It is possible to implement a realistic sleep/awake detection method when all of these factors are taken into account. This means that using machine learning and signal processing methods, the ECG signal can be used to diagnose obstructive sleep apnea.

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Pushing Constraints by Rule-Driven Pruning Techniques in Non-Uniform Minimum Support for Predicting Obstructive Sleep Apnea

Cited by 6 publications

References 12 publications

Pushing Visualization Effects into Pushed Schema Enumerated Tree-Based Support Constraints

Pushing Visualization Effects into Pushed Schema Enumerated Tree-Based Support Constraints

Intellective Site System of University Library on Account of Elevated Apriori Algorithm (AA)

Literature Review on Sleep APNEA Analysis by Machine Learning Algorithms Using ECG Signals

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