A novel medical diagnosis support system for predicting patients with atherosclerosis diseases

Terrada, Oumaima; Cherradi, Bouchaib; Raihani, Abdelhadi; Bouattane, Omar

doi:10.1016/j.imu.2020.100483

Cited by 26 publications

(18 citation statements)

References 31 publications

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“…More recently, ML approaches are being applied to develop predictive models to classify individuals who are likely to be diagnosed with diseases as a result of atherosclerosis. 24 Terrada et al introduce a Medical Diagnosis Support Systems (MDSS) after having achieved an accuracy of 98%. 24 In development, the MDSS was assessed using seven different classification algorithms: Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Naïve Bayes (NB), Classification Ensemble (CE), and Discriminant Analysis (DA).…”

Section: Introductionmentioning

confidence: 99%

“… 24 Terrada et al introduce a Medical Diagnosis Support Systems (MDSS) after having achieved an accuracy of 98%. 24 In development, the MDSS was assessed using seven different classification algorithms: Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Naïve Bayes (NB), Classification Ensemble (CE), and Discriminant Analysis (DA). 24 The MDSS used data from 835 patient medical records who suffer from atherosclerosis, usually caused by coronary artery diseases (CAD), collected from three databases: the Cleveland Heart Disease, Hungarian, and Z-Alizadeh Sani databases.…”

Section: Introductionmentioning

confidence: 99%

“… 24 In development, the MDSS was assessed using seven different classification algorithms: Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Naïve Bayes (NB), Classification Ensemble (CE), and Discriminant Analysis (DA). 24 The MDSS used data from 835 patient medical records who suffer from atherosclerosis, usually caused by coronary artery diseases (CAD), collected from three databases: the Cleveland Heart Disease, Hungarian, and Z-Alizadeh Sani databases. 24 The system's input was a combination of variables included in the final dataset and resulted in an accuracy rating of 98%, far more accurate than other approaches.…”

Section: Introductionmentioning

confidence: 99%

“… 24 The MDSS used data from 835 patient medical records who suffer from atherosclerosis, usually caused by coronary artery diseases (CAD), collected from three databases: the Cleveland Heart Disease, Hungarian, and Z-Alizadeh Sani databases. 24 The system's input was a combination of variables included in the final dataset and resulted in an accuracy rating of 98%, far more accurate than other approaches. 24 …”

Section: Introductionmentioning

confidence: 99%

“… 24 The system's input was a combination of variables included in the final dataset and resulted in an accuracy rating of 98%, far more accurate than other approaches. 24 …”

Section: Introductionmentioning

confidence: 99%

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Application of machine learning in understanding atherosclerosis: Emerging insights

et al. 2021

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Biological processes are incredibly complex—integrating molecular signaling networks involved in multicellular communication and function, thus maintaining homeostasis. Dysfunction of these processes can result in the disruption of homeostasis, leading to the development of several disease processes including atherosclerosis. We have significantly advanced our understanding of bioprocesses in atherosclerosis, and in doing so, we are beginning to appreciate the complexities, intricacies, and heterogeneity atherosclerosi. We are also now better equipped to acquire, store, and process the vast amount of biological data needed to shed light on the biological circuitry involved. Such data can be analyzed within machine learning frameworks to better tease out such complex relationships. Indeed, there has been an increasing number of studies applying machine learning methods for patient risk stratification based on comorbidities, multi-modality image processing, and biomarker discovery pertaining to atherosclerotic plaque formation. Here, we focus on current applications of machine learning to provide insight into atherosclerotic plaque formation and better understand atherosclerotic plaque progression in patients with cardiovascular disease.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“… 24 The system's input was a combination of variables included in the final dataset and resulted in an accuracy rating of 98%, far more accurate than other approaches. 24 …”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations