Primary risk stratification for neonatal jaundice among term neonates using machine learning algorithm

2022 ◽  
Vol 165 ◽  
pp. 105538
Author(s):  
Joshua Guedalia ◽  
Rivka Farkash ◽  
Netanel Wasserteil ◽  
Yair Kasirer ◽  
Misgav Rottenstreich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Stratification ◽  
Neonatal Jaundice ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Term Neonates

scholarly journals Using a machine learning algorithm to predict acute graft-versus-host disease following allogeneic transplantation

2019 ◽  
Vol 3 (22) ◽  
pp. 3626-3634 ◽  
Author(s):  
Yasuyuki Arai ◽  
Tadakazu Kondo ◽  
Kyoko Fuse ◽  
Yasuhiko Shibasaki ◽  
Masayoshi Masuko ◽  
...  
Keyword(s):  
Machine Learning ◽  
Overall Survival ◽  
Risk Stratification ◽  
Learning Algorithm ◽  
Allogeneic Transplantation ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Graft Versus Host ◽  
Key Points ◽  
Acute Graft

Key Points The machine learning algorithms produced clinically reasonable and robust risk stratification scores for aGVHD. Predicting scores for aGVHD also demonstrated the link between risk of development of aGVHD and overall survival after HSCT.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

SMART IDENTIFICATION OF POWER QUALITY EVENTS USING NEW STOCKWELL TRANSFORM AND MACHINE LEARNING ALGORITHM

2019 ◽  
Vol XVI (4) ◽  
pp. 95-113
Author(s):  
Muhammad Tariq ◽  
Tahir Mehmood
Keyword(s):  
Machine Learning ◽  
Power Quality ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Noisy Environments ◽  
Satisfactory Performance ◽  
Feature Values ◽  
Learning Machine ◽  
Stockwell Transform ◽  
Electrical Utilities

Accurate detection, classification and mitigation of power quality (PQ) distortive events are of utmost importance for electrical utilities and corporations. An integrated mechanism is proposed in this paper for the identification of PQ distortive events. The proposed features are extracted from the waveforms of the distortive events using modified form of Stockwell’s transform. The categories of the distortive events were determined based on these feature values by applying extreme learning machine as an intelligent classifier. The proposed methodology was tested under the influence of both the noisy and noiseless environments on a database of seven thousand five hundred simulated waveforms of distortive events which classify fifteen types of PQ events such as impulses, interruptions, sags and swells, notches, oscillatory transients, harmonics, and flickering as single stage events with their possible integrations. The results of the analysis indicated satisfactory performance of the proposed method in terms of accuracy in classifying the events in addition to its reduced sensitivity under various noisy environments.

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