Computational Learning Model for Prediction of Heart Disease Using Machine Learning Based on a New Regularizer

Albahr, Abdulaziz; Albahar, Marwan Ali; Thanoon, Mohammed I.; Binsawad, Muhammad

doi:10.1155/2021/8628335

Cited by 10 publications

(8 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach prevents the learning model from using a wide range of weight space by multiplying the standard deviation of the weight matrix by its parameter to obtain its regularization term. The standard deviation measures the spread of the weight values within the matrix and is computed by taking the square root of the variance of the weights [ [31] , [32] , [33] , [34] , [35] ].

…”

Section: Methodsmentioning

confidence: 99%

Exploring Hajj pilgrim satisfaction with hospitality services through expectation-confirmation theory and deep learning

Albahar,

Gazzawe,

Thanoon

et al. 2023

Heliyon

Self Cite

View full text Add to dashboard Cite

…”

Section: Methodsmentioning

confidence: 99%

Exploring Hajj pilgrim satisfaction with hospitality services through expectation-confirmation theory and deep learning

Albahar,

Gazzawe,

Thanoon

et al. 2023

Heliyon

Self Cite

View full text Add to dashboard Cite

“…Among the applied classifiers, they found that the Random Forest classifier yields the highest accuracy of 96.28%. A computational model for detecting heart abnormality is developed by the authors of [19]. This model incorporates a suggested regularizer with artificial neural network.…”

Section: Related Workmentioning

confidence: 99%

Secure Heart Disease Classification System Based on Three Pass Protocol and Machine Learning

shaker

Obayes

Al-Shareefi

2023

ijcsm

View full text Add to dashboard Cite

Heart disease is one of the worst life-threatening conditions. Correct and early diagnosis of this disease is crucial for saving patients’ life and avoiding other complications. On the other hand, keeping the patient’s data, diagnosis process, and treatment plan secured is equally important to the defactomedical procedure. This research proposes a system that is consisting of two phases: security provision and patients’ condition diagnosis. Typically, the first phase exercises a security protocol, called three-pass protocol, to ensure that the people who can access the patient's information are authorized. In order to obtain a high accuracy level in the diagnosis process, artificial intelligence with machine learning methods are employed in the later phase. The proposed system relies on a data set which includes a number of vital indicators, by which the patient's status can be classified as having heart disease or not. The KNN algorithm and the random forest tree algorithm are applied to carry out the classification task. The accuracy scale results reveals that the randomforest tree algorithm (99%) gave higher accuracy than KNN (97%).

show abstract

“…On the basis of retaining the original multi-dimensional features, we tried to use the embedded method to filter features for the establishment of the sleep staging model. The embedded method is a feature filtering method that uses machine learning algorithms and models to obtain the weight coefficients of each feature and then selects features based on the coefficients from largest to smallest [ 28 ]. If the feature set filtered by the embedded method can achieve the same staging accuracy, the cost of computing sleep staging will be reduced in practical applications.…”

Section: Inductionmentioning

confidence: 99%

Evaluation of a Single-Channel EEG-Based Sleep Staging Algorithm

Zhao

Long

Wei

et al. 2022

IJERPH

View full text Add to dashboard Cite

Sleep staging is the basis of sleep assessment and plays a crucial role in the early diagnosis and intervention of sleep disorders. Manual sleep staging by a specialist is time-consuming and is influenced by subjective factors. Moreover, some automatic sleep staging algorithms are complex and inaccurate. The paper proposes a single-channel EEG-based sleep staging method that provides reliable technical support for diagnosing sleep problems. In this study, 59 features were extracted from three aspects: time domain, frequency domain, and nonlinear indexes based on single-channel EEG data. Support vector machine, neural network, decision tree, and random forest classifier were used to classify sleep stages automatically. The results reveal that the random forest classifier has the best sleep staging performance among the four algorithms. The recognition rate of the Wake phase was the highest, at 92.13%, and that of the N1 phase was the lowest, at 73.46%, with an average accuracy of 83.61%. The embedded method was adopted for feature filtering. The results of sleep staging of the 11-dimensional features after filtering show that the random forest model achieved 83.51% staging accuracy under the condition of reduced feature dimensions, and the coincidence rate with the use of all features for sleep staging was 94.85%. Our study confirms the robustness of the random forest model in sleep staging, which also represents a high classification accuracy with appropriate classifier algorithms, even using single-channel EEG data. This study provides a new direction for the portability of clinical EEG monitoring.

show abstract

Computational Learning Model for Prediction of Heart Disease Using Machine Learning Based on a New Regularizer

Cited by 10 publications

References 39 publications

Exploring Hajj pilgrim satisfaction with hospitality services through expectation-confirmation theory and deep learning

Exploring Hajj pilgrim satisfaction with hospitality services through expectation-confirmation theory and deep learning

Secure Heart Disease Classification System Based on Three Pass Protocol and Machine Learning

Evaluation of a Single-Channel EEG-Based Sleep Staging Algorithm

Contact Info

Product

Resources

About