Sequential Feature Selection and Machine Learning Algorithm-Based Patient’s Death Events Prediction and Diagnosis in Heart Disease

Aggrawal, Ritu; Pal, Saurabh

doi:10.1007/s42979-020-00370-1

Cited by 61 publications

(33 citation statements)

References 31 publications

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“…Forward feature selection is an iterative procedure that begins with the feature having the highest performance versus the target feature. When selecting the feature subset, with support vector machine used as a learning algorithm, stratification is required to guarantee that each class is well represented [4]. Each subset is evaluated independently, and the creation of subsets is determined by the search method.…”

Section: Feature Selection Using Wrapper Methodsmentioning

confidence: 99%

“…(2) Backward Feature Elimination (BFE). e approach of backward elimination is the complete opposite of the method of forward feature selection [4]. e following stages are followed to finish the feature selection procedure.…”

Section: Feature Selection Using Wrapper Methodsmentioning

confidence: 99%

“…Ritu et al [4] presented a sequential feature selection method for identifying mortality events in patients with heart disease during treatment to find the most critical features. Numerous machine learning methods are utilized, including LDA, KNN, RF, SVM, DT, and GBC.…”

Section: Literature Surveymentioning

confidence: 99%

“…However, there were 303 instances and 75 features when this dataset was developed. All published studies [2,4,6,7,12] make use of a selection of 14 features, including biographic features, clinical features, and habitual features in the dataset, along with one output feature. A detailed description of the dataset is provided in Table 1.…”

Section: Data Collectionmentioning

confidence: 99%

“…In addition, computational biomedical systems can speed up the decision-making process and achieve greater prediction accuracy in different diseases such as heart diseases, cancers, diabetes, skin diseases, and kidney diseases. Among these ailments, cardiovascular diseases have been identified as having the highest mortality rate in most countries worldwide [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Comparative Study on Heart Disease Prediction Using Feature Selection Techniques on Classification Algorithms

Dissanayake

Johar

2021

Applied Computational Intelligence and Soft Computing

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Heart disease is recognized as one of the leading factors of death rate worldwide. Biomedical instruments and various systems in hospitals have massive quantities of clinical data. Therefore, understanding the data related to heart disease is very important to improve prediction accuracy. This article has conducted an experimental evaluation of the performance of models created using classification algorithms and relevant features selected using various feature selection approaches. For results of the exploratory analysis, ten feature selection techniques, i.e., ANOVA, Chi-square, mutual information, ReliefF, forward feature selection, backward feature selection, exhaustive feature selection, recursive feature elimination, Lasso regression, and Ridge regression, and six classification approaches, i.e., decision tree, random forest, support vector machine, K-nearest neighbor, logistic regression, and Gaussian naive Bayes, have been applied to Cleveland heart disease dataset. The feature subset selected by the backward feature selection technique has achieved the highest classification accuracy of 88.52%, precision of 91.30%, sensitivity of 80.76%, and f-measure of 85.71% with the decision tree classifier.

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Section: Feature Selection Using Wrapper Methodsmentioning

confidence: 99%

Section: Feature Selection Using Wrapper Methodsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparative Study on Heart Disease Prediction Using Feature Selection Techniques on Classification Algorithms

Dissanayake

Johar

2021

Applied Computational Intelligence and Soft Computing

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Advanced Hybrid Techniques for Cyberattack Detection and Defense in IoT Networks

Mahdi,

Zaki,

Alzubaidi

2024

Security and Privacy

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The Internet of Things (IoT) represents a vast network of devices connected to the Internet, making it easier for users to connect to modern technology. However, the complexity of these networks and the large volume of data pose significant challenges in protecting them from persistent cyberattacks, such as distributed denial‐of‐service (DDoS) attacks and spoofing. It has become necessary to use intrusion detection systems and protect these networks. Existing intrusion detection systems for IoT networks face many problems and limitations, including high false alarm rates and delayed detection. Also, the datasets used for training may be outdated or sparse, which reduces the model's accuracy, and mechanisms may not be used to defend the network when any intrusion is detected. To address these limitations, a new hybrid deep learning and machine learning methodology is proposed that contributes to detecting DDoS and spoofing attacks, reducing false alarms, and then implementing the necessary defensive measures. In proposed hybrid methodology consists of three stages: the first stage is to propose a hybrid method for feature selection consisting of techniques (correlation coefficient and sequential feature selector); the second stage is to propose a hybrid model by integrating deep learning neural networks with a machine learning classifier (cascaded long short‐term memory [LSTM] and Naive Bayes classifier); and in the third stage, improving network defense mechanisms and blocking ports after detecting threats and maintaining network integrity. In training and evaluating the performance of the proposed methodology, three datasets (CIC‐DDoS2019, CIC‐IoT2023, and CIC‐IoV2024) were used, and these data were also balanced to obtain effective results. The accuracy of 99.91%, 99.88%, and 99.77% was obtained. Also, a cross‐validation technique was used with the test data to ensure no overfitting. The proposed methodology has proven its high performance in detecting attacks, as it provides a powerful solution to enhance the security of IoT networks and protect them from cyberattacks, as it can be applied in many fields and to other attacks.

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Comparison of Machine Learning Algorithms and Ensemble Technique for Heart Disease Prediction

Aggarwal

Pal

2021

Advances in Intelligent Systems and Computing

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Sequential Feature Selection and Machine Learning Algorithm-Based Patient’s Death Events Prediction and Diagnosis in Heart Disease

Cited by 61 publications

References 31 publications

Comparative Study on Heart Disease Prediction Using Feature Selection Techniques on Classification Algorithms

Comparative Study on Heart Disease Prediction Using Feature Selection Techniques on Classification Algorithms

Advanced Hybrid Techniques for Cyberattack Detection and Defense in IoT Networks

Comparison of Machine Learning Algorithms and Ensemble Technique for Heart Disease Prediction

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