An explainable predictive model for suicide attempt risk using an ensemble learning and Shapley Additive Explanations (SHAP) approach

Nordin, Noratikah; Zainol, Zurinahni; Noor, Mohd Halim Mohd; Chan, Lai Fong

doi:10.1016/j.ajp.2022.103316

Cited by 44 publications

(24 citation statements)

References 35 publications

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“…Natural Language Processing (NLP) techniques have been used to analyze electronic health records (EHRs) to extract information that can be used to predict or diagnose diseases [ 63 ]. Explainable AI techniques, such as SHAP (SHapley Additive exPlanations), are presented to interpret the predictions made by ML models, which is essential in a medical context where the decision-making should be transparent [ 64 ]. Generative models such as GANs (Generative Adversarial Networks) are invented to generate synthetic medical images that can augment existing data, such as lung disease, and improve the result performance [ 65 ].…”

Section: Artificial Intelligence Techniques In Disease Diagnosis and ...mentioning

confidence: 99%

Evaluation of artificial intelligence techniques in disease diagnosis and prediction

Nia

Kaplanoğlu

Nasab

2023

Discov Artif Intell

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A broad range of medical diagnoses is based on analyzing disease images obtained through high-tech digital devices. The application of artificial intelligence (AI) in the assessment of medical images has led to accurate evaluations being performed automatically, which in turn has reduced the workload of physicians, decreased errors and times in diagnosis, and improved performance in the prediction and detection of various diseases. AI techniques based on medical image processing are an essential area of research that uses advanced computer algorithms for prediction, diagnosis, and treatment planning, leading to a remarkable impact on decision-making procedures. Machine Learning (ML) and Deep Learning (DL) as advanced AI techniques are two main subfields applied in the healthcare system to diagnose diseases, discover medication, and identify patient risk factors. The advancement of electronic medical records and big data technologies in recent years has accompanied the success of ML and DL algorithms. ML includes neural networks and fuzzy logic algorithms with various applications in automating forecasting and diagnosis processes. DL algorithm is an ML technique that does not rely on expert feature extraction, unlike classical neural network algorithms. DL algorithms with high-performance calculations give promising results in medical image analysis, such as fusion, segmentation, recording, and classification. Support Vector Machine (SVM) as an ML method and Convolutional Neural Network (CNN) as a DL method is usually the most widely used techniques for analyzing and diagnosing diseases. This review study aims to cover recent AI techniques in diagnosing and predicting numerous diseases such as cancers, heart, lung, skin, genetic, and neural disorders, which perform more precisely compared to specialists without human error. Also, AI's existing challenges and limitations in the medical area are discussed and highlighted.

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Section: Artificial Intelligence Techniques In Disease Diagnosis and ...mentioning

confidence: 99%

Evaluation of artificial intelligence techniques in disease diagnosis and prediction

Nia

Kaplanoğlu

Nasab

2023

Discov Artif Intell

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“…However, due to the complexity of the non-parametric algorithms that are common in machine-learning methods, it is impossible for a human to analyze each tree and execute an explanation of how the machine-learning method works [ 1 , 62 – 65 ]. Thus, using SHAP allows for a similar covariate interpretation as linear regression even if the exact effect-sizes of the covariates cannot be interpreted the way it can in linear regression [ 15 , 22 , 49 , 66 – 68 ]. Fig 2A highlights the relationship between increasing values of a covariate (purple) and increased odds for heart disease.…”

Section: Discussionmentioning

confidence: 99%

Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations

Huang

2023

PLoS ONE

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Machine learning methods are widely used within the medical field. However, the reliability and efficacy of these models is difficult to assess, making it difficult for researchers to identify which machine-learning model to apply to their dataset. We assessed whether variance calculations of model metrics (e.g., AUROC, Sensitivity, Specificity) through bootstrap simulation and SHapely Additive exPlanations (SHAP) could increase model transparency and improve model selection. Data from the England National Health Services Heart Disease Prediction Cohort was used. After comparison of model metrics for XGBoost, Random Forest, Artificial Neural Network, and Adaptive Boosting, XGBoost was used as the machine-learning model of choice in this study. Boost-strap simulation (N = 10,000) was used to empirically derive the distribution of model metrics and covariate Gain statistics. SHapely Additive exPlanations (SHAP) to provide explanations to machine-learning output and simulation to evaluate the variance of model accuracy metrics. For the XGBoost modeling method, we observed (through 10,000 completed simulations) that the AUROC ranged from 0.771 to 0.947, a difference of 0.176, the balanced accuracy ranged from 0.688 to 0.894, a 0.205 difference, the sensitivity ranged from 0.632 to 0.939, a 0.307 difference, and the specificity ranged from 0.595 to 0.944, a 0.394 difference. Among 10,000 simulations completed, we observed that the gain for Angina ranged from 0.225 to 0.456, a difference of 0.231, for Cholesterol ranged from 0.148 to 0.326, a difference of 0.178, for maximum heart rate (MaxHR) ranged from 0.081 to 0.200, a range of 0.119, and for Age ranged from 0.059 to 0.157, difference of 0.098. Use of simulations to empirically evaluate the variability of model metrics and explanatory algorithms to observe if covariates match the literature are necessary for increased transparency, reliability, and utility of machine learning methods. These variance statistics, combined with model accuracy statistics can help researchers identify the best model for a given dataset.

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“…SHAP is a useful method for sorting effects and breaking down predictions into individual feature impacts [98]. The SHAP value indicates the degree to which a particular feature has changed the prediction, and allows the modeler to decompose any prediction into the sum of the effects of each feature value [99]. The SHAP value is used as a unified measure in measuring feature importance.…”

Section: Shap Values (Shapley Additive Explanations)mentioning

confidence: 99%

Boosting Algorithm to Handle Unbalanced Classification of PM_2.5 Concentration Levels by Observing Meteorological Parameters in Jakarta-Indonesia Using AdaBoost, XGBoost, CatBoost, and LightGBM

et al. 2023

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An explainable predictive model for suicide attempt risk using an ensemble learning and Shapley Additive Explanations (SHAP) approach

Cited by 44 publications

References 35 publications

Evaluation of artificial intelligence techniques in disease diagnosis and prediction

Evaluation of artificial intelligence techniques in disease diagnosis and prediction

Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations

Boosting Algorithm to Handle Unbalanced Classification of PM_2.5 Concentration Levels by Observing Meteorological Parameters in Jakarta-Indonesia Using AdaBoost, XGBoost, CatBoost, and LightGBM

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An explainable predictive model for suicide attempt risk using an ensemble learning and Shapley Additive Explanations (SHAP) approach

Cited by 44 publications

References 35 publications

Evaluation of artificial intelligence techniques in disease diagnosis and prediction

Evaluation of artificial intelligence techniques in disease diagnosis and prediction

Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations

Boosting Algorithm to Handle Unbalanced Classification of PM2.5 Concentration Levels by Observing Meteorological Parameters in Jakarta-Indonesia Using AdaBoost, XGBoost, CatBoost, and LightGBM

Contact Info

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Resources

About

Boosting Algorithm to Handle Unbalanced Classification of PM_2.5 Concentration Levels by Observing Meteorological Parameters in Jakarta-Indonesia Using AdaBoost, XGBoost, CatBoost, and LightGBM