Prediction of mortality risk of health checkup participants using machine learning-based models: the J-SHC study

Kawano, Kazuharu; Otaki, Yoichiro; Suzuki, Naoki; Fujimoto, Shouichi; Iseki, Kunitoshi; Moriyama, Toshiki; Yamagata, Kunihiro; Tsuruya, Kazuhiko; Narita, Ichiei; Kondo, Masahide; Shibagaki, Yugo; Kasahara, Masato; Asahi, Koichi; Watanabe, Takehiko; Konta, Tsuneo

doi:10.1038/s41598-022-18276-8

Cited by 8 publications

(2 citation statements)

References 10 publications

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“…Authors used various combinations (LIME -local interpreted explanations and SHAP). K. Kawano, Y. Otaki et al developed the methods to predict 5-year human mortality based on the data obtained from a medical examination, and identify the consequences of each disease using the SHAP method [37]. Comparative analysis by [38] showed that SHAP value analysis is a promising method for incorporating explainability in model development and usage and might yield better and more trustworthy ML models in the future.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Interpersonal Relationships of Social Network Users Using Explainable Artificial Intelligence Methods

Ustin,

Gafarov,

Berdnikov

2023

OBM Neurobiol

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The emergence of the social networking phenomenon and the sudden spread of the coronavirus pandemic (COVID-19) around the world have significantly affected the transformation of the system of interpersonal relations, partly shifting them towards virtual reality. Online social networks have greatly expanded the boundaries of human interpersonal interaction and initiated processes of integration of different cultures. As a result, research into the possibilities of predicting human behavior through the characteristics of virtual communication in social networks has become more relevant. The aim of the study is: to explore the possibilities of machine learning model interpretability methods for interpreting the success of social network users based on their profile data. This paper uses a specific method of explainable artificial intelligence, SHAP (SHapley Additive exPlanations), to analyze and interpret trained machine learning models. The research is based on Social Network Analysis (SNA), a modern line of research conducted to understand different aspects of the social network as a whole as well as its individual nodes (users). User accounts on social networks provide detailed information that characterizes a user's personality, interests, and hobbies and reflects their current status. Characteristics of a personal profile also make it possible to identify social graphs - mathematical models reflecting the characteristics of interpersonal relationships of social network users. An important tool for social network analysis is various machine learning algorithms that make different predictions based on sets of characteristics (social network data). However, most of today's powerful machine learning methods are "black boxes," and therefore the challenge of interpreting and explaining their results arises. The study trained RandomForestClassifier and XGBClassifier models and showed the nature and degree of influence of the personal profile metrics of VKontakte social network users and indicators of their interpersonal relationship characteristics (graph metrics).

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Section: Literature Reviewmentioning

confidence: 99%

Analysis of Interpersonal Relationships of Social Network Users Using Explainable Artificial Intelligence Methods

Ustin,

Gafarov,

Berdnikov

2023

OBM Neurobiol

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“…The study by Chen et al the found that XGBoost has outstanding prediction capabilities, especially for prediction with tabular data, and its ability to handle missing values is considered a significant advantage. In conclusion, XGBoost is better at handling problems with missing values and large training data than commonly used methods, such as random forests and networks [6]. Based on this, this study will predict the log Differential Time Shear Slowness (DTSM) value using Extreme Gradient Booosting (XGBoost) machine learning algorithm with hyperparameter tuning using grid search technique.…”

Section: Introductionmentioning

confidence: 99%

Sonic Log Prediction Based on Extreme Gradient Boosting (XGBoost) Machine Learning Algorithm by Using Well Log Data

Hakam,

Utama,

Garini

et al. 2024

BIO Web Conf.

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Sonic log is an important aspect that provides a detailed description of the subsurface properties associated with oil and gas reservoirs. The problem that frequently occurs is the unavailability of sonic log data for various reasons needs to be given an effective solution. The alternative approach proposed in this research is sonic log prediction based on Extreme Gradient Boosting (XGBoost) machine learning algorithm, using available log data to build a reliable sonic log prediction model. In this research, the predicted DT log type is the Differential Time Shear Slowness (DTSM) log, which is the velocity of shear waves propagating in a formation. Log features used for training include gamma ray (GR), density (RHOB), porosity (NPHI), resistivity (RS and RD) logs with DTSM log as the prediction target. To optimise the performance and generalisation of the XGBoost algorithm in predicting log DTSM, hyperparameter tuning was applied using grid search technique to obtain optimal parameters for the prediction model. Based on the experimental results, this research found that hyperparameter tuning using grid search technique improved the accuracy of sonic log (DTSM) model prediction based on XGBoost algorithm, as proven by the decrease of RMSE and MAPE values to 19.699 and 7.713%. The results also pointed out the need for methods other than listwise deletion to handle missing values as an alternative to improving model accuracy. This research highlighted the need for continuous improvement in data processing methods and algorithm optimization to advance the application of machine learning in geophysical exploration.

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Quantitative assessment and mitigation strategies of greenhouse gas emissions from rice fields in China: A data-driven approach based on machine learning and statistical modeling

Wang

et al. 2023

Computers and Electronics in Agriculture

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Prediction of mortality risk of health checkup participants using machine learning-based models: the J-SHC study

Cited by 8 publications

References 10 publications

Analysis of Interpersonal Relationships of Social Network Users Using Explainable Artificial Intelligence Methods

Analysis of Interpersonal Relationships of Social Network Users Using Explainable Artificial Intelligence Methods

Sonic Log Prediction Based on Extreme Gradient Boosting (XGBoost) Machine Learning Algorithm by Using Well Log Data

Quantitative assessment and mitigation strategies of greenhouse gas emissions from rice fields in China: A data-driven approach based on machine learning and statistical modeling

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