Sleep Quality Monitoring with Human Assisted Corrections

2022

Self Cite

138

A stroke is caused when blood flow to a part of the brain is stopped abruptly. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. In this research work, with the aid of machine learning (ML), several models are developed and evaluated to design a robust framework for the long-term risk prediction of stroke occurrence. The main contribution of this study is a stacking method that achieves a high performance that is validated by various metrics, such as AUC, precision, recall, F-measure and accuracy. The experiment results showed that the stacking classification outperforms the other methods, with an AUC of 98.9%, F-measure, precision and recall of 97.4% and an accuracy of 98%.

Section: Introductionmentioning

confidence: 99%

Stroke Risk Prediction with Machine Learning Techniques

2022

Self Cite

138

“…Recent advances in the fields of Artificial Intelligence (AI) and Machine Learning (ML) may provide clinicians and physicians with efficient tools for the early diagnosis of various diseases, such as Cholesterol [ 13 ], Hypertension [ 14 ], COPD [ 15 ], Continuous Glucose Monitoring [ 16 ], Short-Term Glucose prediction [ 17 ], COVID-19 [ 18 ], CVDs [ 19 ], Stroke [ 20 ], CKD [ 21 ], ALF [ 22 ], Sleep Disorders [ 23 ], Hepatitis [ 24 ] and Cancer [ 25 ]. The prediction of type 2 diabetes is the point of interest in this research work.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Machine-Learning Methods for Diabetes Risk Prediction

2022

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Diabetes mellitus is a chronic condition characterized by a disturbance in the metabolism of carbohydrates, fats and proteins. The most characteristic disorder in all forms of diabetes is hyperglycemia, i.e., elevated blood sugar levels. The modern way of life has significantly increased the incidence of diabetes. Therefore, early diagnosis of the disease is a necessity. Machine Learning (ML) has gained great popularity among healthcare providers and physicians due to its high potential in developing efficient tools for risk prediction, prognosis, treatment and the management of various conditions. In this study, a supervised learning methodology is described that aims to create risk prediction tools with high efficiency for type 2 diabetes occurrence. A features analysis is conducted to evaluate their importance and explore their association with diabetes. These features are the most common symptoms that often develop slowly with diabetes, and they are utilized to train and test several ML models. Various ML models are evaluated in terms of the Precision, Recall, F-Measure, Accuracy and AUC metrics and compared under 10-fold cross-validation and data splitting. Both validation methods highlighted Random Forest and K-NN as the best performing models in comparison to the other models.

“…In addition, people who sleep a lot or sometimes less than necessary and those who fall asleep at very different times are at greater risk for cardiovascular disease. The more irregular the duration of their sleep, the greater the risk of developing CVD due to a biological clock disorder that affects metabolism, blood pressure and heart rate [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

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

2023

Self Cite

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%.