scholarly journals Health Data Driven on Continuous Blood Pressure Prediction Based on Gradient Boosting Decision Tree Algorithm

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 32423-32433 ◽  
Author(s):  
Bing Zhang ◽  
Jiadong Ren ◽  
Yongqiang Cheng ◽  
Bing Wang ◽  
Zhiyao Wei
Keyword(s):  
Blood Pressure ◽  
Decision Tree ◽  
Health Data ◽  
Data Driven ◽  
Gradient Boosting ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Continuous Blood Pressure

scholarly journals Secondary structural characterization of the nucleic acids from circular dichroism spectra using extreme gradient boosting decision-tree algorithm

2020 ◽  
Author(s):  
Chakkarai Sathyaseelan ◽  
V Vinothini ◽  
Thenmalarchelvi Rathinavelan
Keyword(s):  
Circular Dichroism ◽  
Decision Tree ◽  
Nucleic Acids ◽  
Secondary Structures ◽  
Gradient Boosting ◽  
Decision Tree Algorithm ◽  
Cd Spectra ◽  
Tree Algorithm ◽  
Extreme Gradient Boosting ◽  
Circular Dichroïsm

AbstractNucleic acids exhibit a repertoire of conformational preference depending on the sequence and environment. Circular dichroism (CD) is an important and valuable tool for monitoring such secondary structural conformations of nucleic acids. Nonetheless, the CD spectral diversity associated with these structures poses a challenge in obtaining the quantitative information about the secondary structural content of a given CD spectrum. To this end, the competence of extreme gradient boosting decision-tree algorithm has been exploited here to predict the diverse secondary structures of nucleic acids. A curated library of 610 CD spectra corresponding to 16 different secondary structures of nucleic acids has been developed and used as a training dataset. For a test dataset of 242 CD spectra, the algorithm exhibited the prediction accuracy of 99%. For the sake of accessibility, the entire process is automated and implemented as a webserver, called CD-NuSS (CD to nucleic acids secondary structure) and is freely accessible at https://www.iith.ac.in/cdnuss/. The XGBoost algorithm presented here may also be extended to identify the hybrid nucleic acid topologies in future.

scholarly journals A Data-Driven Assessment of the Metabolic Syndrome Criteria for Adult Health Management in Taiwan

2018 ◽  
Vol 16 (1) ◽  
pp. 92 ◽  
Author(s):  
Ming-Shu Chen ◽  
Shih-Hsin Chen
Keyword(s):  
Metabolic Syndrome ◽  
Decision Tree ◽  
Older People ◽  
Information Gain ◽  
Data Driven ◽  
Decision Tree Algorithm ◽  
Middle Aged ◽  
Tree Structures ◽  
Tree Algorithm ◽  
The Metabolic Syndrome

According to the modified Adult Treatment Panel III, five indices are used to define metabolic syndrome (MetS): waist circumference (WC), high blood pressure, fasting glucose, triglycerides (TG), and high-density lipoprotein cholesterol. Our work evaluates the importance of these indices. In addition, we attempted to identify whether trends and patterns existed among young, middle-aged, and older people. Following the analysis, a decision tree algorithm was used to analyze the importance of the five criteria for MetS because the algorithm in question selects the attribute with the highest information gain as the split node. The most important indices are located on the top of the tree, indicating that these indices can effectively distinguish data in a binary tree and the importance of this criterion. That is, the decision tree algorithm specifies the priority of the influence factors. The decision tree algorithm examined four of the five indices because one was excluded. Moreover, the tree structures differed among the three age groups. For example, the first key index for middle-aged and older people was TG whereas for younger people it was WC. Furthermore, the order of the second to fourth indices differed among the groups. Because the key index was identified for each age group, researchers and practitioners could provide different health care strategies for individuals based on age. High-risk middle-aged and healthy older people maintained low values of TG, which might be the most crucial index. When a person can avoid the first and second indices provided by the decision tree, they are at lower risk of MetS. Therefore, this paper provides a data-driven guideline for MetS prevention.

Sign in / Sign up

Export Citation Format

Share Document