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Beyond the Scoreboard: A Machine Learning Investigation of Online Games’ Influence on Jordanian University Students’ Grades 超越记分牌:网络游戏对约旦大学生成绩影响的机器学习研究
IF 2.9 Q2 Engineering Pub Date : 2024-01-16 DOI: 10.1155/2024/1337725
M. Alshraideh, Abeer Abdel-Jabbar Abu-Zayed, Martin Leiner, Iyad Muhsen AlDajani
In the latter part of the 21st century, the prevalence of online games has significantly increased, encompassing titles connected to the Internet via smart devices, enabling multiplayer interaction. Recent media attention has shed light on the adverse effects associated with online gaming. This research paper explores the viewpoints of 4,700 university students in Jordan regarding the physical, psychological, and behavioural impacts of Internet games. Additionally, it predicts how these impacts may affect the academic performance of 1,410 students. To analyze student trends and forecast outcomes based on sustained game engagement, a convolutional neural network (CNN) was specifically developed for the neural network. The findings revealed student consensus with recommended university measures to limit online game usage, emphasizing a prevalent belief in the negative influence of games on the body, behaviour, and mental health. In terms of the prediction process, the training data encompassed 60%, 70%, and 80% of the dataset. The results revealed that the highest accuracy, 96.69%, was achieved at the 70% threshold for predicting students’ grade point average (GPA). The analysis suggested that projecting a decrease in the percentage of hours dedicated to playing online games could act as a mitigating factor to prevent GPA decline. Consequently, the system advises a range from 99.9% to 4.1%. This implies that a student with a maximum of 99.9% is encouraged to significantly reduce playing hours to preserve their GPA, while a student with a minimum of 4.1% is recommended to decrease playing hours by 4.1%. On average, for the 1,090 students, the system proposes a 48.36% reduction in playing hours to safeguard their GPAs and mitigate potential risks. This high level of accuracy played a crucial role in forecasting students’ GPA outcomes following a year of sustained daily engagement with online games. Notably, the results unveiled a concerning revelation that 80% of students would face a detrimental impact on their academic performance after one year of such consistent online game involvement.
在 21 世纪后半叶,网络游戏的普及率大幅提高,包括通过智能设备连接到互联网、实现多人互动的游戏。最近,媒体关注了与网络游戏相关的负面影响。本研究论文探讨了约旦 4,700 名大学生对网络游戏的生理、心理和行为影响的看法。此外,本文还预测了这些影响可能会如何影响 1410 名学生的学习成绩。为了分析学生的趋势并根据持续参与游戏的情况预测结果,专门为神经网络开发了一个卷积神经网络(CNN)。研究结果表明,学生对学校建议的限制网络游戏使用的措施达成了共识,强调了游戏对身体、行为和心理健康的负面影响的普遍看法。在预测过程中,训练数据分别占数据集的 60%、70% 和 80%。结果显示,在预测学生平均学分绩点(GPA)时,70%的临界值准确率最高,达到96.69%。分析表明,减少玩网络游戏的时间比例可以作为防止 GPA 下降的缓解因素。因此,系统建议的范围为 99.9% 至 4.1%。这意味着,鼓励最高达 99.9% 的学生大幅减少游戏时间,以保持其 GPA,而建议最低达 4.1% 的学生减少 4.1% 的游戏时间。平均而言,在 1090 名学生中,系统建议减少 48.36% 的游戏时间,以保障他们的 GPA 并降低潜在风险。这种高度的准确性在预测学生在每天持续参与网络游戏一年后的 GPA 结果方面发挥了至关重要的作用。值得注意的是,结果揭示了一个令人担忧的问题,即 80% 的学生在持续参与网络游戏一年后,其学业成绩将受到不利影响。
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引用次数: 0
An Intelligent Framework Based on Deep Learning for Online Quran Learning during Pandemic 基于深度学习的智能框架,用于大流行病期间的在线古兰经学习
IF 2.9 Q2 Engineering Pub Date : 2023-12-22 DOI: 10.1155/2023/5541699
Natasha Nigar, Amna Wajid, S. A. Ajagbe, Matthew O. Adigun
The COVID-19 pandemic influenced the whole world and changed social life globally. Social distancing is an effective strategy adopted by all countries to prevent humans from being infected. Al-Quran is the holy book of Muslims and its listening and reading is one of the obligatory activities. Close contact is essential in traditional learning system; however, most of the Al-Quran learning schools were locked down to minimize the spread of COVID-19 infection. To address this limitation, in this paper, we propose a novel system using deep learning to identify the correct recitation of individual alphabets, words from a recited verse and a complete verse of Al-Quran to assist the reciter. Moreover, in the proposed approach, if the user recites correctly, his/her voice is also added to the existing dataset to leverage proposed approach effectiveness. We employ mel-frequency cepstral coefficients (MFCC) to extract voice features and long short-term memory (LSTM), a recurrent neural network (RNN) for classification. The said approach is validated using the Al-Quran dataset. The results demonstrate that the proposed system outperforms the state-of-the-art approaches with an accuracy rate of 97.7%. This system will help the Muslim community all over the world to recite the Al-Quran in the right way in the absence of human help due to similar future pandemics.
COVID-19 大流行影响了整个世界,改变了全球的社会生活。拉开社会距离是所有国家为防止人类受到感染而采取的有效策略。古兰经》是穆斯林的圣书,聆听和阅读《古兰经》是穆斯林的必修课之一。在传统的学习系统中,密切接触是必不可少的;然而,大多数古兰经学习学校都被封锁,以尽量减少 COVID-19 感染的传播。为了解决这一局限性,我们在本文中提出了一种新颖的系统,利用深度学习来识别《古兰经》中单个字母、诵读经文中的单词和完整经文的正确诵读,从而为诵读者提供帮助。此外,在建议的方法中,如果用户背诵正确,他/她的声音也会被添加到现有的数据集中,以提高建议方法的有效性。我们采用梅尔频率倒频谱系数(MFCC)来提取语音特征,并利用长短期记忆(LSTM)和循环神经网络(RNN)进行分类。上述方法使用《古兰经》数据集进行了验证。结果表明,所提出的系统优于最先进的方法,准确率高达 97.7%。该系统将帮助世界各地的穆斯林社区在未来类似的大流行病发生时,在没有人类帮助的情况下以正确的方式诵读《古兰经》。
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引用次数: 0
Performance Augmentation of Base Classifiers Using Adaptive Boosting Framework for Medical Datasets 针对医学数据集使用自适应提升框架增强基础分类器的性能
IF 2.9 Q2 Engineering Pub Date : 2023-12-22 DOI: 10.1155/2023/5542049
Durr e Nayab, Rehan Ullah Khan, A. M. Qamar
This paper investigates the performance enhancement of base classifiers within the AdaBoost framework applied to medical datasets. Adaptive boosting (AdaBoost), being an instance of boosting, combines other classifiers to enhance their performance. We conducted a comprehensive experiment to assess the efficacy of twelve base classifiers with the AdaBoost framework, namely, Bayes network, decision stump, ZeroR, decision tree, Naïve Bayes, J-48, voted perceptron, random forest, bagging, random tree, stacking, and AdaBoost itself. The experiments are carried out on five datasets from the medical domain based on various types of cancers, i.e., global cancer map (GCM), lymphoma-I, lymphoma-II, leukaemia, and embryonal tumours. The evaluation focuses on the accuracy, precision, and efficiency of the base classifiers in the AdaBoost framework. The results show that the performance of Naïve Bayes, Bayes network, and voted perceptron is highly improved compared to the rest of the base classifiers, attaining accuracies as high as 94.74%, 97.78%, and 97.78%, respectively. The results also show that in most cases, the base classifiers perform better with AdaBoost compared to their performance, i.e., for voted perceptron, the accuracy is improved up to 13.34%.For bagging, it is improved by up to 7%. This research aims to identify such base classifiers with optimal boosting capabilities within the AdaBoost framework for medical datasets. The significance of these results is that they provide insight into the performance of the base classifiers when used in the boosting framework to enhance the classification performance of classifiers in scenarios where individual classifiers do not perform up to the mark.
本文研究了将 AdaBoost 框架应用于医疗数据集的基础分类器的性能提升。自适应提升(AdaBoost)作为提升的一个实例,结合了其他分类器以提高其性能。我们进行了一项综合实验,以评估 AdaBoost 框架下十二种基础分类器的功效,它们分别是贝叶斯网络、决策桩、ZeroR、决策树、奈夫贝叶斯、J-48、投票感知器、随机森林、bagging、随机树、堆叠和 AdaBoost 本身。实验在医学领域的五个数据集上进行,这些数据集基于不同类型的癌症,即全球癌症图谱(GCM)、淋巴瘤-I、淋巴瘤-II、白血病和胚胎肿瘤。评估的重点是 AdaBoost 框架中基础分类器的准确度、精确度和效率。结果表明,与其他基础分类器相比,奈夫贝叶斯、贝叶斯网络和投票感知器的性能有了很大提高,准确率分别高达 94.74%、97.78% 和 97.78%。结果还显示,在大多数情况下,使用 AdaBoost 后,基础分类器的表现比它们的表现更好,例如,投票感知器的准确率提高了 13.34%,而装袋分类器的准确率提高了 7%。这项研究的目的是在 AdaBoost 框架内为医学数据集确定具有最佳提升能力的基础分类器。这些结果的意义在于,当基础分类器被用于提升框架时,它们能让人深入了解其性能,从而在单个分类器性能不达标的情况下提高分类器的分类性能。
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引用次数: 0
Corrigendum to “An Efficient Blind Image Deblurring Using a Smoothing Function” 对 "使用平滑函数的高效盲图像去模糊 "的更正
IF 2.9 Q2 Engineering Pub Date : 2023-12-09 DOI: 10.1155/2023/9812479
Kittiya Khongkraphan, Aniruth Phonon, Sainuddeen Nuiphom
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引用次数: 0
Aspect-Based Sentiment Analysis for Afaan Oromoo Movie Reviews Using Machine Learning Techniques 利用机器学习技术对阿凡-奥罗莫语电影评论进行基于方面的情感分析
IF 2.9 Q2 Engineering Pub Date : 2023-12-07 DOI: 10.1155/2023/3462691
Obsa Gelchu Horsa, K. K. Tune
Aspect-based sentiment analysis (ABSA) is the subfield of natural language processing that deals with essentially splitting data into aspects and finally extracting the sentiment polarity as positive, negative, or neutral. ABSA has been widely investigated and developed for many resource-rich languages such as English and French. However, little work has been done on indigenous African languages like Afaan Oromoo both at the document and sentence levels. In this paper, ABSA for Afaan Oromoo movie reviews was investigated and developed. To achieve the proposed objective, 2800 Afaan Oromoo movie reviews were collected from YouTube using YouTube Data API. Following the data preprocessing, predetermined aspects of the Afaan Oromoo movie were extracted and labeled into positive or negative aspects by domain experts. For implementation, different machine learning algorithms including random forest, logistic regression, SVM, and multinomial naïve Bayes in combination with BoW and TF-IDF were applied. To test and measure the proposed system, accuracy, precision, recall, and f1-score were used. In the case of random forest, the accuracy obtained in combination with both BoW and TF-IDF was 88%. Using the SVM, the accuracy generated with BoW and TF-IDF was 88% and 87%, respectively. Applying logistic regression, the accuracy generated with both BoW and TF-IDF was 87%. Using multinomial naïve Bayes, the accuracy generated in combination with both BoW and TF-IDF was 88%. To improve the optimal performance evaluation parameters, different hyperparameter tuning settings were applied. The implementation result shows that the optimal values of models’ performance evaluation parameters were generated using different hyperparameter tuning settings.
基于方面的情感分析(ABSA)是自然语言处理的一个子领域,它主要处理将数据分割成方面,并最终提取出积极、消极或中性的情感极性。针对英语和法语等资源丰富的语言,ABSA已经得到了广泛的研究和开发。然而,在文件和句子层面上,对Afaan Oromoo等非洲土著语言的研究却很少。本文对Afaan Oromoo电影评论的ABSA进行了研究和开发。为了实现所提出的目标,使用YouTube Data API从YouTube上收集了2800条Afaan Oromoo电影评论。在数据预处理之后,由领域专家提取Afaan Oromoo电影的预定方面并标记为积极或消极方面。为了实现,我们使用了不同的机器学习算法,包括随机森林、逻辑回归、SVM和多项naïve Bayes,并结合BoW和TF-IDF。为了测试和测量所提出的系统,准确度,精密度,召回率和f1-score被使用。在随机森林的情况下,结合BoW和TF-IDF获得的准确率为88%。使用SVM, BoW和TF-IDF生成的准确率分别为88%和87%。应用逻辑回归,BoW和TF-IDF产生的准确率均为87%。使用多项naïve Bayes,结合BoW和TF-IDF生成的准确率为88%。为了提高最优的性能评价参数,采用了不同的超参数调优设置。实现结果表明,使用不同的超参数调优设置,可以生成模型性能评价参数的最优值。
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引用次数: 0
Applications of Quantum Probability Amplitude in Decision Support Systems 量子概率振幅在决策支持系统中的应用
IF 2.9 Q2 Engineering Pub Date : 2023-12-07 DOI: 10.1155/2023/5532174
S. Payandeh
Establishing various frameworks for managing uncertainties in decision-making systems have been posing many fundamental challenges to the system design engineers. Quantum paradigm has been introduced to the area of decision and control communities as a possible supporting platform in such uncertainty management. This paper presents an overview of how a quantum framework and, in particular, probability amplitude has been proposed and utilized in the literature to complement two classical probabilistic decision-making approaches. The first such framework is based in the Bayesian network, and the second is based on an element of Dempster–Shafer (DS) theory using the definition of mass function. The paper first presents a summary of these classical approaches, followed by a review of their preliminary enhancements using the quantum model framework. Particular attention was given on how the notion of probability amplitude is utilized in such extensions to the quantum-like framework. Numerical walk-through examples are combined with the presentation of each method in order to better demonstrate the extensions of the proposed frameworks. The main objective is to better define and develop a common platform in order to further explore and experiment with this alternative framework as a part of a decision support system.
建立各种框架来管理决策系统中的不确定性已经对系统设计工程师提出了许多根本性的挑战。量子范式已经被引入决策和控制社区领域,作为这种不确定性管理的可能支持平台。本文概述了如何在文献中提出和利用量子框架,特别是概率振幅来补充两种经典的概率决策方法。第一个这样的框架是基于贝叶斯网络的,第二个是基于使用质量函数定义的Dempster-Shafer (DS)理论的一个元素。本文首先介绍了这些经典方法的总结,然后回顾了它们使用量子模型框架的初步增强。特别注意了概率振幅的概念如何在这种类量子框架的扩展中被利用。数值演练示例与每种方法的演示相结合,以便更好地演示所提出框架的扩展。主要目标是更好地定义和开发一个公共平台,以便进一步探索和试验将此替代框架作为决策支持系统的一部分。
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引用次数: 0
Image-Based Arabic Sign Language Recognition System Using Transfer Deep Learning Models 使用迁移深度学习模型的基于图像的阿拉伯手语识别系统
IF 2.9 Q2 Engineering Pub Date : 2023-12-06 DOI: 10.1155/2023/5195007
Qanita Bani Baker, Nour Alqudah, Tibra Alsmadi, Rasha Awawdeh
Sign language is a unique communication tool helping to bridge the gap between people with hearing impairments and the general public. It holds paramount importance for various communities, as it allows individuals with hearing difficulties to communicate effectively. In sign languages, there are numerous signs, each characterized by differences in hand shapes, hand positions, motions, facial expressions, and body parts used to convey specific meanings. The complexity of visual sign language recognition poses a significant challenge in the computer vision research area. This study presents an Arabic Sign Language recognition (ArSL) system that utilizes convolutional neural networks (CNNs) and several transfer learning models to automatically and accurately identify Arabic Sign Language characters. The dataset used for this study comprises 54,049 images of ArSL letters. The results of this research indicate that InceptionV3 outperformed other pretrained models, achieving a remarkable 100% accuracy score and a 0.00 loss score without overfitting. These impressive performance measures highlight the distinct capabilities of InceptionV3 in recognizing Arabic characters and underscore its robustness against overfitting. This enhances its potential for future research in the field of Arabic Sign Language recognition.
手语是一种独特的沟通工具,有助于弥合听力障碍人士与公众之间的差距。它对各个社区至关重要,因为它使听力有困难的人能够有效地沟通。在手语中,有许多手势,每个手势都有不同的手部形状、手部位置、动作、面部表情和用来传达特定含义的身体部位。视觉手语识别的复杂性对计算机视觉研究领域提出了重大挑战。本文提出了一种利用卷积神经网络(cnn)和多种迁移学习模型自动准确识别阿拉伯手语字符的阿拉伯手语识别系统(ArSL)。本研究使用的数据集包括54,049张ArSL字母图像。本研究结果表明,InceptionV3优于其他预训练模型,在没有过拟合的情况下,实现了100%的准确率得分和0.00的损失得分。这些令人印象深刻的性能指标突出了InceptionV3在识别阿拉伯字符方面的独特能力,并强调了它对过拟合的鲁棒性。这增强了其在阿拉伯语手语识别领域未来研究的潜力。
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引用次数: 0
Conditional Tabular Generative Adversarial Net for Enhancing Ensemble Classifiers in Sepsis Diagnosis 用于增强败血症诊断中的集合分类器的条件表生成对抗网
IF 2.9 Q2 Engineering Pub Date : 2023-11-25 DOI: 10.1155/2023/8819052
A. Alfakeeh, M. S. Sharif, A. Zorto, Thiago Pillonetto
Antibiotic-resistant bacteria have proliferated at an alarming rate as a result of the extensive use of antibiotics and the paucity of new medication research. The possibility that an antibiotic-resistant bacterial infection would progress to sepsis is one of the major collateral problems affecting people with this condition. 31,000 lives were lost due to sepsis in England with costs about two billion pounds annually. This research aims to develop and evaluate several classification approaches to improve predicting sepsis and reduce the tendency of underdiagnosis in computer-aided predictive tools. This research employs medical datasets for patients diagnosed with sepsis, and it analyses the efficacy of ensemble machine learning techniques compared to nonensemble machine learning techniques and the significance of data balancing and conditional tabular generative adversarial nets for data augmentation in producing reliable diagnosis. The average F Score obtained by the nonensemble models trained in this paper is 0.83 compared to the ensemble techniques average of 0.94. Nonensemble techniques, such as Decision Tree, achieved an F score of 0.90, an AUC of 0.90, and an accuracy of 90%. Histogram-basedgradient boosting classification tree achieved an F score of 0.96, an AUC of 0.96, and an accuracy of 95%, surpassing the other models tested. Additionally, when compared to the current state-of-the-art sepsis prediction models, the models developed in this study demonstrated higher average performance in all metrics, indicating reduced bias and improved robustness through data balancing and conditional tabular generative adversarial nets for data augmentation. The study revealed that data balancing and augmentation on the ensemble machine learning algorithms boost the efficacy of clinical predictive models and can help clinics decide which data types are most important when examining patients and diagnosing sepsis early through intelligent human-machine interface.
由于抗生素的广泛使用和新药物研究的匮乏,抗生素耐药细菌以惊人的速度激增。抗生素耐药细菌感染发展成败血症的可能性是影响败血症患者的主要附带问题之一。在英国,有 31,000 人因败血症而丧生,每年的损失约为 20 亿英镑。这项研究旨在开发和评估几种分类方法,以改进败血症的预测,减少计算机辅助预测工具中诊断不足的倾向。这项研究采用了被诊断为败血症患者的医疗数据集,分析了集合机器学习技术与非集合机器学习技术的功效对比,以及数据平衡和条件表生成对抗网的数据增强在产生可靠诊断方面的意义。本文中训练的非集合模型获得的平均 F 分数为 0.83,而集合技术的平均 F 分数为 0.94。非集合技术(如决策树)的 F 得分为 0.90,AUC 为 0.90,准确率为 90%。基于直方图的梯度提升分类树的 F 值为 0.96,AUC 为 0.96,准确率为 95%,超过了其他测试模型。此外,与目前最先进的败血症预测模型相比,本研究中开发的模型在所有指标上都表现出更高的平均性能,这表明通过数据平衡和条件表生成对抗网进行数据增强,减少了偏差并提高了鲁棒性。研究表明,在集合机器学习算法上进行数据平衡和增强可提高临床预测模型的功效,并能帮助诊所在检查病人时决定哪些数据类型最重要,通过智能人机界面及早诊断败血症。
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引用次数: 0
Three-Axes Mems Calibration Using Kalman Filter and Delaunay Triangulation Algorithm 使用卡尔曼滤波器和 Delaunay 三角测量算法进行三轴微系统校准
IF 2.9 Q2 Engineering Pub Date : 2023-11-22 DOI: 10.1155/2023/7658064
Anwer Sabah Ahmed, Qais Al-Gayem
MEMS-IMUs are widely used in research, industry, and commerce. A proper calibration technique must reduce their innate errors. In this study, a turntable-based IMU calibration approach was presented. Parameters such as the bias, lever arm, and scale factor, in addition to misalignment, are included in the general nonlinear model of the IMU output. Accelerometer error parameters were estimated using the transformed unscented Kalman filter (TUKF) with triangulation algorithm is suggested for calibrating inertial measurement unit (MPU6050) three-axes accelerometer. In contrast to the present methods, the suggested method uses the gravitational signal as a constant reference and necessitates no external equipment. The technique requires that the sensor be positioned in a rough orientation and that basic rotations be adopted. This technology also offers a quicker and easier calibration. Comparing the experimental findings with other works, Allan deviation shows significant improvements for the bias instability, where a bias instability of (0.116 μg) is achieved at temperatures between (−15°C) and (80°C).
MEMS-IMU 广泛应用于科研、工业和商业领域。适当的校准技术必须减少其固有误差。本研究提出了一种基于转盘的 IMU 校准方法。在 IMU 输出的一般非线性模型中,除了失准之外,还包括偏置、杠杆臂和比例因子等参数。在校准惯性测量单元(MPU6050)三轴加速度计时,建议使用带有三角测量算法的变换无特征卡尔曼滤波器(TUKF)估算加速度计误差参数。与现有方法相比,建议的方法使用重力信号作为恒定参考,无需外部设备。该技术要求将传感器放置在一个大致的方位,并采用基本的旋转。这项技术还能提供更快、更简便的校准。将实验结果与其他工作进行比较,Allan 偏差显示偏差不稳定性有了显著改善,在温度为(-15°C)和(80°C)之间,偏差不稳定性达到了(0.116 μg)。
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引用次数: 0
An Intelligent Diagnostic System to Analyze Early-Stage Chronic Kidney Disease for Clinical Application 分析早期慢性肾病的智能诊断系统在临床中的应用
IF 2.9 Q2 Engineering Pub Date : 2023-11-22 DOI: 10.1155/2023/3140270
N. I. Md. Ashafuddula, Bayezid Islam, Rafiqul Islam
Chronic kidney disease (CKD) is a progressive condition characterized by the gradual deterioration of kidney functions, potentially leading to kidney failure if not promptly diagnosed and treated. Machine learning (ML) algorithms have shown significant promise in disease diagnosis, but in healthcare, clinical data pose challenges: missing values, noisy inputs, and redundant features, affecting early-stage CKD prediction. Thus, this study presents a novel, fully automated machine learning approach to tackle these complexities by incorporating feature selection (FS) and feature space reduction (FSR) techniques, leading to a substantial enhancement of the model’s performance. A data balancing technique is also employed during preprocessing to address data imbalance issue that is commonly encountered in clinical contexts. Finally, for reliable CKD classification, an ensemble characteristics-based classifier is encouraged. The effectiveness of our approach is rigorously validated and assessed on multiple datasets, and the clinical relevancy of the strategy is evaluated on the real-world therapeutic data collected from Bangladeshi patients. The study establishes the dominance of adaptive boosting, logistic regression, and passive aggressive ML classifiers with 96.48% accuracy in forecasting unseen therapeutic CKD data, particularly in early-stage cases. Furthermore, the effectiveness of the FSR technique in reducing the prediction time significantly is revealed. The outstanding performance of the proposed model demonstrates its effectiveness in addressing the complexity of healthcare CKD data by incorporating the FS and FSR techniques. This highlights its potential as a promising computer-aided diagnosis tool for doctors, enabling early interventions and improving patient outcomes.
慢性肾脏病(CKD)是一种渐进性疾病,其特点是肾功能逐渐恶化,如果不及时诊断和治疗,有可能导致肾衰竭。机器学习(ML)算法在疾病诊断方面已显示出显著的前景,但在医疗保健领域,临床数据带来了挑战:缺失值、噪声输入和冗余特征,影响了早期 CKD 预测。因此,本研究提出了一种新颖的全自动机器学习方法,通过结合特征选择(FS)和特征空间缩小(FSR)技术来解决这些复杂问题,从而大大提高了模型的性能。在预处理过程中还采用了数据平衡技术,以解决临床中经常遇到的数据不平衡问题。最后,为了实现可靠的 CKD 分类,我们鼓励使用基于集合特征的分类器。我们的方法在多个数据集上进行了严格的验证和评估,并在从孟加拉国患者收集的真实世界治疗数据上评估了该策略的临床相关性。研究结果表明,在预测未见的 CKD 治疗数据(尤其是早期病例)时,自适应提升、逻辑回归和被动攻击型 ML 分类器的准确率高达 96.48%,占据主导地位。此外,FSR 技术还能有效缩短预测时间。所提模型的出色表现表明,通过结合 FS 和 FSR 技术,该模型能有效处理复杂的医疗慢性肾病数据。这凸显了该模型作为医生的计算机辅助诊断工具的潜力,可实现早期干预并改善患者预后。
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引用次数: 0
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Applied Computational Intelligence and Soft Computing
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