A nested stacking ensemble model for predicting districts with high and low maternal mortality ratio (MMR) in India

Shastri, Sourabh; Kour, Paramjit; Kumar, Sachin; Singh, Kuljeet; Sharma, Anand; Mansotra, Vibhakar

doi:10.1007/s41870-020-00560-3

Cited by 7 publications

(2 citation statements)

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“…RNNs looping networks can take input and raw knowledge from the earlier networks, process the information, and give better predictability about the results to the networks next to it [ 16 ]. RNNs are not capable of learning long-term dependencies from the historical time series data but it still works better than exponential smoothing and autoregressive integrated moving average models [ 17 ]. Because of its incompetency to model long-term dependencies there exists the vanishing and exploding gradient problems in RNN which leads to the inception of Long Short Term Memory (LSTM) to overcome these limitations.…”

Section: Methodsmentioning

confidence: 99%

CoBiD-net: a tailored deep learning ensemble model for time series forecasting of covid-19

et al. 2021

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The pandemic of novel coronavirus disease 2019 (Covid-19) has left the world to a standstill by creating a calamitous situation. To mitigate this devastating effect the inception of artificial intelligence into medical health care is mandatory. This study aims to present the educational perspective of Covid-19 and forecast the number of confirmed and death cases in the USA, India, and Brazil along with the discussion of endothelial dysfunction in epithelial cells and Angiotensin-Converting Enzyme 2 receptor (ACE2) with the Covid-19. Three different deep learning based experimental setups have been framed to forecast Covid-19. Models are (i) Bi-directional Long Short Term Memory (LSTM) (ii) Convolutional LSTM (iii) Proposed ensemble of Convolutional and Bi-directional LSTM network are known as CoBiD-Net ensemble. The educational perspective of Covid-19 has been given along with an architectural discussion of multi-organ failure due to intrusion of Covid-19 with the cell receptors of the human body. Different classification metrics have been calculated using all three models. Proposed CoBiD-Net ensemble model outperforms the other two models with respect to accuracy and mean absolute percentage error (MAPE). Using CoBiD-Net ensemble, accuracy for Covid-19 cases ranges from 98.10 to 99.13% with MAPE ranges from 0.87 to 1.90. This study will help the countries to know the severity of Covid-19 concerning education in the future along with forecasting of Covid-19 cases and human body interaction with the Covid-19 to make it the self-replicating phenomena. Supplementary Information The online version contains supplementary material available at 10.1007/s41324-021-00408-3.

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Section: Methodsmentioning

confidence: 99%

CoBiD-net: a tailored deep learning ensemble model for time series forecasting of covid-19

et al. 2021

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“…Furthermore, Zou et al [4] and Petrozziello et al [5] proposed models for predicting adverse delivery outcomes and identifying fetal compromise during labour and delivery; both studies highlight the potential of hybrid approaches in predicting maternal outcomes [6]- [9]. Based on this premise, ConvXGB emerges as a superior choice over traditional models in maternal health prediction.…”

Section: Introductionmentioning

confidence: 99%

Unlocking Maternal Outcome Prediction Potential: A Comprehensive Analysis of the ConvXGB Model Integrating XGBoost and Deep Learning

Nwokoro,

Akinnuwesi,

Shastri

et al. 2024

Preprint

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The significance of maternal health cannot be overemphasized, and the ability to predict maternal outcomes accurately is critical to ensuring the well-being of both mothers and infants. This study presents ConvXGB, a novel predictive model that utilizes a combination of XGBoost, a potent gradient boosting algorithm, and deep learning to extract intricate features. The objective is to enhance precision and robustness of maternal outcome predictions. The study sourced diverse maternal health data from the southern region of Nigeria and implemented Synthetic Minority Over-sampling Technique (SMOTE) to address any dataset imbalances. Results obtain demonstrate a significant improvement in model performance, with an accuracy rate of 97.96% across various maternal outcome classes. The recommendations from this study highlight the potential of ConvXGB in advancing maternal health predictive analytics, supporting informed clinical decision-making, and improving resource allocation. Further studies are warranted to explore the broader applicability of ConvXGB in different healthcare domains through outcome analyses and methodological advancements.

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NestEn_SmVn: boosted nested ensemble multiplexing to diagnose coronary artery disease

et al. 2021

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A nested stacking ensemble model for predicting districts with high and low maternal mortality ratio (MMR) in India

Cited by 7 publications

References 50 publications

CoBiD-net: a tailored deep learning ensemble model for time series forecasting of covid-19

CoBiD-net: a tailored deep learning ensemble model for time series forecasting of covid-19

Unlocking Maternal Outcome Prediction Potential: A Comprehensive Analysis of the ConvXGB Model Integrating XGBoost and Deep Learning

NestEn_SmVn: boosted nested ensemble multiplexing to diagnose coronary artery disease

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