Towards a precision medicine Solution for optimal pediatric Laparoscopy: An exploratory data analysis for features Selections

Carnevale, Lorenzo; Floramo, Giuseppe; Fabrizio, Donatella Di; Arena, Salvatore; Montalto, Angela Simona; Impellizzeri, Pietro; Romeo, Carmelo; Villari, Massimo

doi:10.1016/j.bspc.2023.105321

Cited by 1 publication

(1 citation statement)

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“…Exploratory data analysis (EDA) [31] is a method used to understand relationships in data. This statistical method is widely used in various fields including medicine, finance, social media, and fighting crime, to reduce the complexity of data and reveal underlying patterns [32][33][34][35].…”

Section: Overview Of Exploratory Data Analysismentioning

confidence: 99%

Prediction and Factor Analysis of Liquefaction Ground Subsidence Based on Machine-Learning Techniques

Karimai,

Liu,

Maruyama

2024

Applied Sciences

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Liquefaction is a significant challenge in the fields of earthquake risk assessment and soil dynamics, as it has the potential to cause extensive damage to buildings and infrastructure through ground failure. During the 2011 Great East Japan Earthquake, Urayasu City in the Chiba Prefecture experienced severe soil liquefaction, leading to evacuation losses due to the effect of the liquefaction on roads. Therefore, developing quantitative predictions of ground subsidence caused by liquefaction and understanding its contributing factors are imperative in preparing for potential future mega-earthquakes. This research is novel because previous research primarily focused on developing predictive models for determining the presence or absence of liquefaction, and there are few examples available of quantitative liquefaction magnitude after liquefaction has occurred. This research study extracts features from existing datasets and builds a predictive model, supplemented by factor analysis. Using the Cabinet Office of Japan’s Nankai Trough Megathrust Earthquake model, liquefaction-induced ground subsidence was designated as the dependent variable. A gradient-boosted decision-tree (GDBT) prediction model was then developed. Additionally, the Shapley additive explanations (SHAP) method was employed to analyze the contribution of each feature to the prediction results. The study found that the XGBoost model outperformed the LightGBM model in terms of predictive accuracy, with the predicted values closely aligned with the actual measurements, thereby proving its effectiveness in predicting ground subsidence due to liquefaction. Furthermore, it was demonstrated that liquefaction assessments, which were previously challenging, can now be interpreted using SHAP factors. This enables accountable wide-area prediction of liquefaction-induced ground subsidence.

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