Stroke risk prediction with machine learning techniques
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 …
blood supply, the brain cells gradually die, and disability occurs depending on the area of …
FW-SMOTE: A feature-weighted oversampling approach for imbalanced classification
Abstract The Synthetic Minority Over-sampling Technique (SMOTE) is a well-known
resampling strategy that has been successfully used for dealing with the class-imbalance …
resampling strategy that has been successfully used for dealing with the class-imbalance …
Lung cancer risk prediction with machine learning models
The lungs are the center of breath control and ensure that every cell in the body receives
oxygen. At the same time, they filter the air to prevent the entry of useless substances and …
oxygen. At the same time, they filter the air to prevent the entry of useless substances and …
Machine learning techniques for chronic kidney disease risk prediction
Chronic kidney disease (CKD) is a condition characterized by progressive loss of kidney
function over time. It describes a clinical entity that causes kidney damage and affects the …
function over time. It describes a clinical entity that causes kidney damage and affects the …
RCSMOTE: Range-Controlled synthetic minority over-sampling technique for handling the class imbalance problem
P Soltanzadeh, M Hashemzadeh - Information Sciences, 2021 - Elsevier
Abstract The Synthetic Minority Over-Sampling Technique (SMOTE) is one of the most well
known methods to solve the unequal class distribution problem in imbalanced datasets …
known methods to solve the unequal class distribution problem in imbalanced datasets …
Supervised machine learning models for liver disease risk prediction
The liver constitutes the largest gland in the human body and performs many different
functions. It processes what a person eats and drinks and converts food into nutrients that …
functions. It processes what a person eats and drinks and converts food into nutrients that …
Data-driven machine-learning methods for diabetes risk prediction
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 …
carbohydrates, fats and proteins. The most characteristic disorder in all forms of diabetes is …
COVID-19 diagnosis by routine blood tests using machine learning
Physicians taking care of patients with COVID-19 have described different changes in
routine blood parameters. However, these changes hinder them from performing COVID-19 …
routine blood parameters. However, these changes hinder them from performing COVID-19 …
Real-time accident detection: Coping with imbalanced data
Detecting accidents is of great importance since they often impose significant delay and
inconvenience to road users. This study compares the performance of two popular machine …
inconvenience to road users. This study compares the performance of two popular machine …
A new oversampling method based on the classification contribution degree
Data imbalance is a thorny issue in machine learning. SMOTE is a famous oversampling
method of imbalanced learning. However, it has some disadvantages such as sample …
method of imbalanced learning. However, it has some disadvantages such as sample …