Interpretability of machine learning‐based prediction models in healthcare
There is a need of ensuring that learning (ML) models are interpretable. Higher
interpretability of the model means easier comprehension and explanation of future …
interpretability of the model means easier comprehension and explanation of future …
Interpretable artificial intelligence in radiology and radiation oncology
Artificial intelligence has been introduced to clinical practice, especially radiology and
radiation oncology, from image segmentation, diagnosis, treatment planning and prognosis …
radiation oncology, from image segmentation, diagnosis, treatment planning and prognosis …
Using machine learning models to forecast severity level of traffic crashes by R Studio and ArcGIS
This study describes crash causes, conditions, and distribution of accident hot spots along
with an analysis of the risk factors that significantly affect severity levels of crashes and their …
with an analysis of the risk factors that significantly affect severity levels of crashes and their …
Prediction of construction accident outcomes based on an imbalanced dataset through integrated resampling techniques and machine learning methods
Purpose Central to the entire discipline of construction safety management is the concept of
construction accidents. Although distinctive progress has been made in safety management …
construction accidents. Although distinctive progress has been made in safety management …
[PDF][PDF] A deep-learned type-3 fuzzy system and its application in modeling problems
MW Tian, A Mohammadzadeh, J Tavoosi… - Acta Polytechnica …, 2022 - academia.edu
The modeling problem is one of the important topics in engineering applications. In various
applications, it is required to find a mathematical model to represent the relationship …
applications, it is required to find a mathematical model to represent the relationship …
[PDF][PDF] The Prediction of Diseases Using Rough Set Theory with Recurrent Neural Network in Big Data Analytics.
V Talasila, K Madhubabu, MC Mahadasyam… - International Journal of …, 2020 - inass.org
In a modern life, early healthcare prediction plays an important role to prevent the loss of life
caused by prediction delays in treatment. Nowadays, the researchers focused on the Big …
caused by prediction delays in treatment. Nowadays, the researchers focused on the Big …
Development of an explainable prediction model of heart failure survival by using ensemble trees
PA Moreno-Sanchez - … international conference on big data (big …, 2020 - ieeexplore.ieee.org
Cardiovascular diseases (CVD) are the leading cause of death globally. Heart failure
prediction, one of the CVD manifestations, has become a priority for doctors, however, up to …
prediction, one of the CVD manifestations, has become a priority for doctors, however, up to …
K 近邻和加权相似性的密度峰值聚类算法.
赵嘉， 陈磊， 吴润秀， 张波… - Control Theory & …, 2022 - search.ebscohost.com
密度峰值聚类算法的局部密度定义未考虑密度分布不均数据类簇间的样本密度差异影响,
易导致误选类簇中心; 其分配策略依据欧氏距离通过密度峰值进行链式分配 …
易导致误选类簇中心; 其分配策略依据欧氏距离通过密度峰值进行链式分配 …
Improvement of a prediction model for heart failure survival through explainable artificial intelligence
PA Moreno-Sanchez - Frontiers in Cardiovascular Medicine, 2023 - frontiersin.org
Cardiovascular diseases and their associated disorder of heart failure (HF) are major
causes of death globally, making it a priority for doctors to detect and predict their onset and …
causes of death globally, making it a priority for doctors to detect and predict their onset and …
A hyper-parameter tuning approach for cost-sensitive support vector machine classifiers
In machine learning, hyperparameter tuning is strongly useful to improve model
performance. In our research, we concentrate our attention on classifying imbalanced data …
performance. In our research, we concentrate our attention on classifying imbalanced data …