Stroke Risk Prediction with Machine Learning Techniques

Δρίτσας, Ηλίας; Trigka, Maria

doi:10.3390/s22134670

Cited by 114 publications

(63 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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

Δρίτσας

Trigka

2022

Sensors

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Data-Driven Machine-Learning Methods for Diabetes Risk Prediction

Δρίτσας

Trigka

2022

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, current stroke risk prediction guidelines use either logistic regression or survival analysis (Chun et al, 2021 ). Additionally, most machine learning articles published recently are comparative in nature, showing the improvement in the performance of different machine learning techniques over the more traditional methods or showing which machine learning technique gives the best performance (Li et al, 2019 ; Shoily et al, 2019 ; Chun et al, 2021 ; Dritsas and Trigka, 2022 ; Lip et al, 2022 ) This is an important step in improving the field, however, such papers do not focus on risk concepts. In fact, most recent machine learning risk prediction papers do not mention the type of risk that is calculated (relative, absolute, hazard ratio, odds ratios etc.)…”

Section: Risk Modelsmentioning

confidence: 99%

A review of risk concepts and models for predicting the risk of primary stroke

Hunter

Kelleher

2022

Front. Neuroinform.

View full text Add to dashboard Cite

Predicting an individual's risk of primary stroke is an important tool that can help to lower the burden of stroke for both the individual and society. There are a number of risk models and risk scores in existence but no review or classification designed to help the reader better understand how models differ and the reasoning behind these differences. In this paper we review the existing literature on primary stroke risk prediction models. From our literature review we identify key similarities and differences in the existing models. We find that models can differ in a number of ways, including the event type, the type of analysis, the model type and the time horizon. Based on these similarities and differences we have created a set of questions and a system to help answer those questions that modelers and readers alike can use to help classify and better understand the existing models as well as help to make necessary decisions when creating a new model.

show abstract

“…Information and communication technologies (ICTs), and especially the fields of artificial intelligence (AI) and machine learning (ML), are moving in this direction. ML techniques now play an important role in the early diagnosis of various diseases, such as diabetes (as classification [ 12 ] or regression task for continuous glucose prediction [ 13 , 14 ]), hypertension [ 15 ], COPD [ 16 ], COVID-19 [ 17 ], CVDs [ 18 ], stroke [ 19 ], CKD [ 20 ], ALF [ 21 ], hepatitis [ 22 ], sleep disorders [ 23 ], cancer [ 24 ], etc.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Methods for Hypercholesterolemia Long-Term Risk Prediction

Δρίτσας

Trigka

2022

Sensors

Self Cite

View full text Add to dashboard Cite

Cholesterol is a waxy substance found in blood lipids. Its role in the human body is helpful in the process of producing new cells as long as it is at a healthy level. When cholesterol exceeds the permissible limits, it works the opposite, causing serious heart health problems. When a person has high cholesterol (hypercholesterolemia), the blood vessels are blocked by fats, and thus, circulation through the arteries becomes difficult. The heart does not receive the oxygen it needs, and the risk of heart attack increases. Nowadays, machine learning (ML) has gained special interest from physicians, medical centers and healthcare providers due to its key capabilities in health-related issues, such as risk prediction, prognosis, treatment and management of various conditions. In this article, a supervised ML methodology is outlined whose main objective is to create risk prediction tools with high efficiency for hypercholesterolemia occurrence. Specifically, a data understanding analysis is conducted to explore the features association and importance to hypercholesterolemia. These factors are utilized to train and test several ML models to find the most efficient for our purpose. For the evaluation of the ML models, precision, recall, accuracy, F-measure, and AUC metrics have been taken into consideration. The derived results highlighted Soft Voting with Rotation and Random Forest trees as base models, which achieved better performance in comparison to the other models with an AUC of 94.5%, precision of 92%, recall of 91.8%, F-measure of 91.7% and an accuracy equal to 91.75%.

show abstract

Stroke Risk Prediction with Machine Learning Techniques

Cited by 114 publications

References 60 publications

Data-Driven Machine-Learning Methods for Diabetes Risk Prediction

Data-Driven Machine-Learning Methods for Diabetes Risk Prediction

A review of risk concepts and models for predicting the risk of primary stroke

Machine Learning Methods for Hypercholesterolemia Long-Term Risk Prediction

Contact Info

Product

Resources

About