Predicted Number of Pregnant Women in Aichi Prefecture, Japan: Estimation by Machine Learning Database Construction for Disaster Preparation

2021 ◽  
pp. 1-9
Author(s):  
Kanetoshi Hattori ◽  
Ritsuko Hattori
Keyword(s):  
Machine Learning ◽  
Pregnant Women ◽  
Disaster Preparedness ◽  
Correlation Coefficients ◽  
Database Construction ◽  
Aichi Prefecture ◽  
Cascade Correlation ◽  
Correlation Learning ◽  
Disaster Preparation ◽  
Area Data

Abstract Aichi prefecture, Japan is predicted to be hit by Mega-earthquake. Aichi Prefectural Association of Midwives has been making efforts to improve disaster preparedness for pregnant women. This project aims to acquire area data of pregnant women for simulated studies of rescue activities. Number of women in census survey areas in Nagoya City was acquired from nationwide data of pregnant women by machine learning (Cascade-Correlation Learning Architecture). Quite high correlation coefficients between actual data and estimation data were observed. Rescue simulations have been carried out based on the data acquired by this study.

scholarly journals Functional Correlations in the Pursuit of Performance Assessment of Classifiers

2020 ◽  
Vol 34 (13) ◽  
pp. 2051013
Author(s):  
Nadezhda Gribkova ◽  
Ričardas Zitikis
Keyword(s):  
Machine Learning ◽  
Performance Assessment ◽  
Correlation Coefficients ◽  
Statistical Classification ◽  
Measures Of Association ◽  
Confusion Matrices

In statistical classification and machine learning, as well as in social and other sciences, a number of measures of association have been proposed for assessing and comparing individual classifiers, raters, as well as their groups. In this paper, we introduce, justify, and explore several new measures of association, which we call CO-, ANTI-, and COANTI-correlation coefficients, that we demonstrate to be powerful tools for classifying confusion matrices. We illustrate the performance of these new coefficients using a number of examples, from which we also conclude that the coefficients are new objects in the sense that they differ from those already in the literature.

Neural Network Studies. 3. Variable Selection in the Cascade-Correlation Learning Architecture

1998 ◽  
Vol 38 (4) ◽  
pp. 651-659 ◽  
Author(s):  
Vasyl V. Kovalishyn ◽  
Igor V. Tetko ◽  
Alexander I. Luik ◽  
Vladyslav V. Kholodovych ◽  
Alessandro E. P. Villa ◽  
...  
Keyword(s):  
Neural Network ◽  
Variable Selection ◽  
Cascade Correlation ◽  
Correlation Learning ◽  
Network Studies

scholarly journals Adherence to Mediterranean Diet and Maternal Lifestyle during Pregnancy: Island–Mainland Differentiation in the CRIBS Birth Cohort

Nutrients ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2179
Author(s):  
Dubravka Havaš Auguštin ◽  
Jelena Šarac ◽  
Mario Lovrić ◽  
Jelena Živković ◽  
Olga Malev ◽  
...  
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Pregnant Women ◽  
Mediterranean Diet ◽  
Dietary Habits ◽  
Island Population ◽  
Learning Approach ◽  
Modifiable Factors ◽  
Machine Learning Approach ◽  
The Mediterranean

Maternal nutrition and lifestyle in pregnancy are important modifiable factors for both maternal and offspring’s health. Although the Mediterranean diet has beneficial effects on health, recent studies have shown low adherence in Europe. This study aimed to assess the Mediterranean diet adherence in 266 pregnant women from Dalmatia, Croatia and to investigate their lifestyle habits and regional differences. Adherence to the Mediterranean diet was assessed through two Mediterranean diet scores. Differences in maternal characteristics (diet, education, income, parity, smoking, pre-pregnancy body mass index (BMI), physical activity, contraception) with regards to location and dietary habits were analyzed using the non-parametric Mann–Whitney U test. The machine learning approach was used to reveal other potential non-linear relationships. The results showed that adherence to the Mediterranean diet was low to moderate among the pregnant women in this study, with no significant mainland–island differences. The highest adherence was observed among wealthier women with generally healthier lifestyle choices. The most significant mainland–island differences were observed for lifestyle and socioeconomic factors (income, education, physical activity). The machine learning approach confirmed the findings of the conventional statistical method. We can conclude that adverse socioeconomic and lifestyle conditions were more pronounced in the island population, which, together with the observed non-Mediterranean dietary pattern, calls for more effective intervention strategies.

scholarly journals Association of serum neutrophil gelatinase associated lipocalin (NGAL) levels with preeclampsia

2019 ◽  
Vol 8 (2) ◽  
pp. 513
Author(s):  
M. Dhivya ◽  
Chippy Tess Mathew ◽  
G. Jeyachandran
Keyword(s):  
Blood Pressure ◽  
Pregnant Women ◽  
Gestational Age ◽  
Correlation Coefficients ◽  
Neutrophil Gelatinase Associated Lipocalin ◽  
Multiple Organs ◽  
Systemic Disorder ◽  
Neutrophil Gelatinase ◽  
And Control ◽  
Control Study

Background: Preeclampsia is a systemic disorder that affects multiple organs and is characterized by the new onset of hypertension and proteinuria or end-organ dysfunction or both in the second half of pregnancy. NGAL is a 25-KDa protein of the lipocalin family and is considered to be a novel biomarker for ischemic injury. The objective of this study is to compare the levels of serum NGAL in preeclamptic patients and gestational age matched normotensive controls.Methods: The study design is case control study in which pregnant women with preeclampsia (n=40) are selected as cases. Cases were selected from pregnant women attending OG-OPD and IP satisfying the inclusion criteria and not coming under exclusion criteria. 0.5ml of blood was collected in vacutainers and was centrifuged at 3500rpm for 10 minutes. The serum thus separated was aliquoted into smaller plain containers and stored at -20 degree Celsius for analysis. The urine sample was also collected. Controls were also selected from the OP patients.Results: In present study, the serum NGAL ranged from 40-900ng/ml in cases and from 110-795ng/ml in controls. There is no difference in NGAL between cases and control. The correlation coefficients between the NGAL levels and other parameters like maternal age, gestational age, systolic Blood pressure, diastolic Blood pressure, uric acid levels, urine PCR are also not statistically significant.Conclusions: Serum NGAL levels are not significantly elevated in patients with preeclampsia when compared with the normotensive controls and also there is no significant correlation between serum NGAL levels and other assessed parameters.

Diesel Engine Acoustic Diagnostics With Machine Learning During Various Degradation Modes

2019 ◽  
Author(s):  
Patrick Moore ◽  
Dianne Luning Prak ◽  
Len Hamilton ◽  
Jim Cowart
Keyword(s):  
Machine Learning ◽  
Diesel Engine ◽  
Fourier Transforms ◽  
Correlation Coefficients ◽  
Supervised Machine Learning ◽  
Normal Operation ◽  
Self Organizing Map ◽  
Engine Operation ◽  
Acoustic Data ◽  
Degradation Modes

Abstract A diesel engine electrical generator set (‘gen-set’) was instrumented with an in-cylinder pressure indicating system as well as an acoustic emission sensor near the engine. Air filter clogging, rocker arm gap and fuel cetane changes were applied during which engine combustion and acoustic data were collected. Fast Fourier Transforms (FFTs) were analyzed on the acoustic data. FFT data were then applied to categorical supervised machine learning neural network analysis with MATLAB based tools. The detection of the various degradation modes was audibly determined with correlation coefficients greater than 99% on test data. Next, an unsupervised machine learning Self Organizing Map (SOM) was produced during normal-baseline operation of the engine. Application of the degraded mode engine sound data from operation with the various faults were then applied to the normal-baseline SOM. The quantization error of the various degraded engine data showed clear statistical differentiation from the normal operation data map. This unsupervised SOM based approach does not know the engine degradation behavior in advance, yet shows promise as a method to monitor and detect changing engine operation. Companion in-cylinder combustion data shows how changing combustion characteristics result in emitted sound differences.

Estimation of Pregnant Women Possibly Left Behind in Tsunami Predicted Areas of Nagoya City: A Simulation for Rescue Planning

2020 ◽  
pp. 1-8
Author(s):  
Ritsuko Hattori ◽  
Shoko Miyagawa ◽  
Kanetoshi Hattori
Keyword(s):  
Machine Learning ◽  
Pregnant Women ◽  
Simulation Study ◽  
Nankai Trough ◽  
Learning Model ◽  
Center Line ◽  
Left Behind ◽  
Machine Learning Model ◽  
The City

ABSTRACT Objective: In case of an outbreak of Nankai Trough Mega-earthquake, it is predicted that a tsunami would invade Nagoya City within 100 minutes, hitting about one third of the City of Nagoya. If the administrative plan of the city and midwives’ expertise are coordinated, pregnant women’s chances of survival will increase. The authors carried out this simulation study in an attempt to improve consistency of the two efforts. Method: We estimated the number of pregnant women using a machine learning model. The evacuation distance of pregnant women was estimated on the basis of the data of road center line. Results: Through this simulation study, it became clear that preparation for approximately 2600 pregnant women escaping from tsunami predicted area and for about 1200 pregnant women possibly left in the area is needed. Conclusions: Our study suggests that triage point planning is needed in areas where pregnant women are evacuated. The triage makes it possible to transport women to appropriate hospitals.

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