Sleep Quality Evaluation in Rich Information Data

2023

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The heart is the most vital organ of the human body; thus, its improper functioning has a significant impact on human life. Coronary artery disease (CAD) is a disease of the coronary arteries through which the heart is nourished and oxygenated. It is due to the formation of atherosclerotic plaques on the wall of the epicardial coronary arteries, resulting in the narrowing of their lumen and the obstruction of blood flow through them. Coronary artery disease can be delayed or even prevented with lifestyle changes and medical intervention. Long-term risk prediction of coronary artery disease will be the area of interest in this work. In this specific research paper, we experimented with various machine learning (ML) models after the use or non-use of the synthetic minority oversampling technique (SMOTE), evaluating and comparing them in terms of accuracy, precision, recall and an area under the curve (AUC). The results showed that the stacking ensemble model after the SMOTE with 10-fold cross-validation prevailed over the other models, achieving an accuracy of 90.9 %, a precision of 96.7%, a recall of 87.6% and an AUC equal to 96.1%.

Section: Introductionmentioning

confidence: 99%

Long-Term Coronary Artery Disease Risk Prediction with Machine Learning Models

2023

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“…Moreover, ML techniques now enable medical researchers to detect significant diseases in a more sophisticated and accurate way. In this direction, ML plays an essential role in the early prediction of disease complications in diabetes (as classification [ 23 , 24 ] or regression task for continuous glucose prediction [ 25 , 26 ]), cholesterol [ 27 ], hypertension [ 28 , 29 ], hypercholesterolemia [ 30 ], chronic obstructive pulmonary disease (COPD) [ 31 ], COVID-19 [ 32 ], stroke [ 33 ], chronic kidney disease (CKD) [ 34 ], liver disease [ 35 ], hepatitis-C [ 36 ], lung cancer [ 37 ], sleep disorders [ 38 ], metabolic syndrome [ 39 ], etc.…”

Section: Introductionmentioning

confidence: 99%

Efficient Data-Driven Machine Learning Models for Cardiovascular Diseases Risk Prediction

2023

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Cardiovascular diseases (CVDs) are now the leading cause of death, as the quality of life and human habits have changed significantly. CVDs are accompanied by various complications, including all pathological changes involving the heart and/or blood vessels. The list of pathological changes includes hypertension, coronary heart disease, heart failure, angina, myocardial infarction and stroke. Hence, prevention and early diagnosis could limit the onset or progression of the disease. Nowadays, machine learning (ML) techniques have gained a significant role in disease prediction and are an essential tool in medicine. In this study, a supervised ML-based methodology is presented through which we aim to design efficient prediction models for CVD manifestation, highlighting the SMOTE technique’s superiority. Detailed analysis and understanding of risk factors are shown to explore their importance and contribution to CVD prediction. These factors are fed as input features to a plethora of ML models, which are trained and tested to identify the most appropriate for our objective under a binary classification problem with a uniform class probability distribution. Various ML models were evaluated after the use or non-use of Synthetic Minority Oversampling Technique (SMOTE), and comparing them in terms of Accuracy, Recall, Precision and an Area Under the Curve (AUC). The experiment results showed that the Stacking ensemble model after SMOTE with 10-fold cross-validation prevailed over the other ones achieving an Accuracy of 87.8%, Recall of 88.3%, Precision of 88% and an AUC equal to 98.2%.

“…Such algorithms have a field of application in various sectors. Machine Learning now has a significant contribution in the field of medicine for the prediction of various diseases and the early diagnosis of several chronic conditions such as diabetes (as classification [ 21 , 22 ] or times-series task for continuous glucose values forecasting [ 23 , 24 ]), high blood pressure (hypertension) [ 25 , 26 ], cholesterol [ 27 , 28 ], chronic obstructive pulmonary disease (COPD) [ 29 ], stroke [ 30 ], cardiovascular diseases (CVDs) [ 31 ], acute liver failure (ALF) [ 32 ], acute lymphoblastic leukemia [ 33 ], sleep disorders [ 34 , 35 ], hepatitis C [ 36 ], lung cancer [ 37 ], chronic kidney disease (CKD) [ 38 ], etc.…”

Section: Introductionmentioning

confidence: 99%

Supervised Machine Learning Models to Identify Early-Stage Symptoms of SARS-CoV-2

2022

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The coronavirus disease (COVID-19) pandemic was caused by the SARS-CoV-2 virus and began in December 2019. The virus was first reported in the Wuhan region of China. It is a new strain of coronavirus that until then had not been isolated in humans. In severe cases, pneumonia, acute respiratory distress syndrome, multiple organ failure or even death may occur. Now, the existence of vaccines, antiviral drugs and the appropriate treatment are allies in the confrontation of the disease. In the present research work, we utilized supervised Machine Learning (ML) models to determine early-stage symptoms of SARS-CoV-2 occurrence. For this purpose, we experimented with several ML models, and the results showed that the ensemble model, namely Stacking, outperformed the others, achieving an Accuracy, Precision, Recall and F-Measure equal to 90.9% and an Area Under Curve (AUC) of 96.4%.