Machine Learning-Based Comparative Study For Heart Disease Prediction

Gullu, Merve; Akçayol, M. Ali; Barışçı, Necaattin

doi:10.54569/aair.1145616

Cited by 2 publications

(1 citation statement)

References 36 publications

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“…Table 2 shows the confusion matrix containing the values for performance metrics. The values in the confusion matrix show the actual values and the estimation values [30]. The fact that the value with a positive label in reality also has a positive label in the prediction part makes it a true positive (TP).…”

Section: The Performance Analysis Metrics In Attack Detectionmentioning

confidence: 99%

A Multi-Model Proposal for Classification and Detection of DDoS Attacks on SCADA Systems

SÖĞÜT

Erdem

2023

Applied Sciences

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Industrial automation and control systems have gained increasing attention in the literature recently. Their integration with various systems has triggered considerable developments in critical infrastructure systems. With different network structures, these systems need to communicate with each other, work in an integrated manner, be controlled, and intervene effectively when necessary. Supervision Control and Data Acquisition (SCADA) systems are mostly utilized to achieve these aims. SCADA systems, which control and monitor the connected systems, have been the target of cyber attackers. These systems are subject to cyberattacks due to the openness to external networks, remote controllability, and SCADA-architecture-specific cyber vulnerabilities. Protecting SCADA systems on critical infrastructure systems against cyberattacks is an important issue that concerns governments in many aspects such as economics, politics, transport, communication, health, security, and reliability. In this study, we physically demonstrated a scaled-down version of a real water plant via a Testbed environment created including a SCADA system. In order to disrupt the functioning of the SCADA system in this environment, five attack scenarios were designed by performing various DDoS attacks, i.e., TCP, UDP, SYN, spoofing IP, and ICMP Flooding. Additionally, we evaluated a scenario with the baseline behavior of the SCADA system that contains no attack. During the implementation of the scenarios, the SCADA system network was monitored, and network data flow was collected and recorded. CNN models, LSTM models, hybrid deep learning models that amalgamate CNN and LSTM, and traditional machine learning models were applied to the obtained data. The test results of various DDoS attacks demonstrated that the hybrid model and the decision tree model are the most suitable for such environments, reaching the highest test accuracy of 95% and 99%, respectively. Moreover, we tested the hybrid model on a dataset that is used commonly in the literature which resulted in 98% accuracy. Thus, it is suggested that the security of the SCADA system can be effectively improved, and we demonstrated that the proposed models have a potential to work in harmony on real field systems.

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Section: The Performance Analysis Metrics In Attack Detectionmentioning

confidence: 99%

A Multi-Model Proposal for Classification and Detection of DDoS Attacks on SCADA Systems

SÖĞÜT

Erdem

2023

Applied Sciences

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Hybrid machine learning techniques based on genetic algorithm for heart disease detection

Kumar,

Dhanka,

Singh

et al. 2024

Innov. Emerg. Technol.

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This research study addresses the critical global health issue of heart disease (HD), emphasizing the importance of early detection for improving recovery outcomes. The authors have applied various machine learning (ML) algorithms, including logistic regression (LR), linear discriminant analysis (LDA), Gaussian naive bayes (GNB), support vector machine (SVM), and XGBoost (XGB) to classify the Statlog and Cleveland HD datasets. Performance metrics such as accuracy, precision, recall, F1-score, specificity, and Cohen’s kappa have been evaluated across these HD records. This study conducted two experiments: one using default ML classifiers and another with a hybrid genetic algorithm ML (GA-ML) model. The GA has been employed as a feature selector (FS), significantly enhancing the performance of each default classifier by selecting 9 out of 13 features. Notably, the GA-XGB model achieved the highest performance with an accuracy of 94.83%, precision of 93.33%, sensitivity of 96.55%, F1-score of 94.52%, specificity of 93.10%, Cohen’s kappa of 0.90, a positive likelihood ratio (LR[Formula: see text]) of 14, a negative likelihood ratio (LR-) of 0.037, and a diagnostic odds ratio (DOR) of 378 on the combined HD dataset. These results have been validated using a 10-fold cross-validation technique. A comparative analysis has been conducted with default ML classifiers, hybrid GA-ML classifiers, and state-of-the-art methods. The results of the GA-ML models confirm the superiority of the proposed method, offering valuable insights into advancing early detection strategies and improving heart health care outcomes.

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Machine Learning-Based Comparative Study For Heart Disease Prediction

Cited by 2 publications

References 36 publications

A Multi-Model Proposal for Classification and Detection of DDoS Attacks on SCADA Systems

A Multi-Model Proposal for Classification and Detection of DDoS Attacks on SCADA Systems

Hybrid machine learning techniques based on genetic algorithm for heart disease detection

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