REMOVED: Machine learning in health condition check-up: An approach using Breiman's random forest algorithm

Algani, Yousef Methkal Abd; Ritonga, Mahyudin; Bala, B. Kiran; Ansari, Mohammed Saleh Al; Badr, Malek; Taloba, Ahmed I.

doi:10.1016/j.measen.2022.100406

Cited by 17 publications

(7 citation statements)

References 11 publications

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“…The Random Forest (RF) classifier is a method that concurrently trains multiple decision trees using bootstrapping and then aggregates the results through a process known as bagging (Fig. 2) [27]. Bootstrapping involves training distinct decision trees simultaneously on various subsets of the training dataset, utilizing different subsets of the available features.…”

Section: ) Rfmentioning

confidence: 99%

Examining the Various Neural Network Algorithms Considering the Superiority of Mouth Brooding Fish in Data Classification

Liu,

Zhu

2024

ijacsa

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Data classification, a crucial practice in information management, involves categorizing data based on its sensitivity to determine appropriate access levels and protection measures. This paper explores the utilization of novel algorithms, including mouth-brooding fish (MBF), alongside machine learning techniques, for the analysis of medical health data. The SVM exhibits suboptimal performance in the task of data categorization. Therefore, Adaboost may be considered a viable substitute for MBF due to its superior performance in terms of Fscore, accuracy, specificity, and sensitivity. The accuracy of MBF, which stands at about 95%, surpasses that of Adaboost by a significant margin of 77%. The F-score, accuracy, and specificity values obtained for MBF are exceptional when compared to the other chosen models, with values of 97.17%, 93.6%, and 96.5%, respectively. The proposed algorithm exhibits promising advancements in health data categorization, offering a potential breakthrough in data classification methodologies. Leveraging this innovative approach could facilitate more accurate and efficient management of sensitive medical data, thereby enhancing healthcare systems' capabilities for data protection and analysis. The main novelty of this study lies in the introduction and evaluation of the MBF algorithm for data classification within the medical domain. Unlike traditional algorithms, MBF draws inspiration from the collective behavior of mouth-brooding fish, offering a unique optimization strategy that enhances both exploration and exploitation of the solution space. This novel approach presents a promising avenue for advancing healthcare analytics and decision-making processes.

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Section: ) Rfmentioning

confidence: 99%

Examining the Various Neural Network Algorithms Considering the Superiority of Mouth Brooding Fish in Data Classification

Liu,

Zhu

2024

ijacsa

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“…The Random Forest classifier combines the judgments made by individual trees in order to get a final conclusion, allowing it to demonstrate strong generalization capabilities. In comparison to other classification approaches, the Random Forest (RF) classifier often achieves superior accuracy while avoiding the problem of overfitting [19].…”

Section: ) Random Forest (Rf)mentioning

confidence: 99%

Comparative Study: Mouth Brooding Fish (MBF) as a Novel Approach for Android Malware Detection

Zhou,

Wang,

2024

ijacsa

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Android Malware Detection has become increasingly prevalent, with the highest market share among all other mobile operating systems due to its open-source nature and user-friendliness. This has resulted in an uncontrolled proliferation of malicious applications targeting the Android platform. Emerging trends of Android malware are employing highly sophisticated detection and analysis evasion techniques, rendering traditional signature-based detection methods less effective in identifying modern and unknown malware.Alternative approaches, such as Machine Learning methods, have emerged as leading solutions for timely zero-day anomaly detection. Ensemble learning, a common meta-approach in machine learning, seeks to improve predictive performance by amalgamating predictions from multiple models. This paper introduces an enhanced strategy, Mouth Brooding Fish (MBF), based on ensemble learning for Android Malware Detection (AMD). The findings are further compared with the outputs of various algorithms including Support Vector Machine (SVM), AdaBoost, Multilayer Perceptron (MLP), Gaussian Kernel (GK), and Random Forest (RF). Compared to the other selected models, MBF exhibits remarkable performance with an F-score of 98.57%, precision of 99.65%, sensitivity of 97.51%, and specificity of 97.51%. Thus, the significant novelty of this work lies in the accuracy and authenticity of the selected algorithms, demonstrating their superior performance overall.

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“…The stator, rotor bars, static and dynamic air gaps, bent shafts, misalignments, bearing failures, and gearbox failures were the primary areas of focus for the majority of the methods that were utilized for the machine health monitoring system. All of these flaws required the analysis of humans or a specific sensor or calculation to be detected [6].…”

Section: Related Workmentioning

confidence: 99%

Machine Learning in Molecular Communication and Applications for Health Monitoring Networks

Kumar

Sharma

et al. 2023

Preprint

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The world has been greatly affected by increased utilization of mobile methods as well as smart devices in field of health. Health professionals are increasingly utilizing these technologies' advantages, resulting in a significant improvement in clinical health care. For this purpose, machine learning (ML)as well as Internet of Things (IoT) can be utilized effectively. This study aims to propose a novel data analysis method for a health monitoring system based on machine learning. Goal of research is to create a ML based smart health monitoring method. It lets doctors keep an eye on patients from a distance as well as take periodic actions if they need to. Utilizing wearable sensors, a set of five parameters—the electrocardiogram (ECG), pulse rate, pressure, temperature, and position detection—have been identified. Kernelized component vector neural networks are used to choose the features in the input data. Then, a sparse attention-based convolutional neural network with a structural search algorithm was used to classify the selected features. For a variety of datasets, the proposed technique attained validation accuracy 95%, training accuracy 92%, RMSE 52%, F-measure 53%, sensitivity 77%.

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REMOVED: Machine learning in health condition check-up: An approach using Breiman's random forest algorithm

Cited by 17 publications

References 11 publications

Examining the Various Neural Network Algorithms Considering the Superiority of Mouth Brooding Fish in Data Classification

Examining the Various Neural Network Algorithms Considering the Superiority of Mouth Brooding Fish in Data Classification

Comparative Study: Mouth Brooding Fish (MBF) as a Novel Approach for Android Malware Detection

Machine Learning in Molecular Communication and Applications for Health Monitoring Networks

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