Disease prediction via Bayesian hyperparameter optimization and ensemble learning

Gao, Liyuan; Ding, Yongmei

doi:10.1186/s13104-020-05050-0

Cited by 28 publications

(15 citation statements)

References 22 publications

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“…In 2020 at Wuhan, Gao and Ding compared machine learning strategies to predict breast cancer (BC) and cardiovascular disease (CVD) in datasets from the Irving repository (14). They found a 94.74% accuracy for the XGBoost model in BC patients, with the ne needle image of breast lump as the most important predictive value, and a 73.5% accuracy for the CVD dataset XGBoost model, with systolic blood pressure as the most important feature.…”

Section: Discussionmentioning

confidence: 99%

Who’s your data? Primary immune deficiency differential diagnosis prediction via machine learning and data mining of the USIDNET registry

Barrera

Guzman

Cascajares³

et al. 2022

Preprint

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INTRODUCTION: There are currently more than 450 primary immune deficiency (PID) diseases, and about 7,000 rare diseases that together afflict around 1 in every 17 humans. Computational aids based on data mining and machine learning might facilitate the diagnostic task by extracting rules from large datasets and making predictions when faced with new problem cases. OBJECTIVE: In a proof-of-concept data mining study, we aimed to predict PID diagnoses with a supervised machine learning algorithm based on classification tree boosting. METHODS: Through a data query at the USIDNET registry we obtained a database of 2,396 patients with common diagnoses of PID, including their clinical and laboratory features. We kept 12 diagnoses and 286 features that were included in the model. We used the XGBoost package with parallel tree boosting for the supervised classification model, and SHAP for variable importance interpretation, on Python v3.7. The patient database was split into training and testing subsets, and after boosting through gradient descent, the predictive model provides measures of diagnostic prediction accuracy and individual feature importance. To correct for imbalanced classification, after a baseline performance test, we used the Class Weighting Hyperparameter, or scale_pos_weight. RESULTS: The twelve PID diagnoses were CVID (1,098 patients), DiGeorge syndrome, Chronic granulomatous disease, Congenital agammaglobulinemia, ID not otherwise classified, Specific antibody deficiency, Complement deficiency, Hyper-IgM, Leukocyte adhesion deficiency, ectodermal dysplasia with immune deficiency, Severe combined immune deficiency, and Wiskott-Aldrich syndrome. For CVID, the model found an accuracy on the train sample of 0.80, with an area under the ROC curve (AUC) of 0.80, and a Gini coefficient of 0.60. In the test subset, accuracy was 0.76, AUC 0.75, and Gini 0.51. The positive feature value to predict CVID was highest for upper respiratory infections, asthma, autoimmunity and hypogammaglobulinemia. Features with the highest negative predictive value were high IgE, growth delay, abscess, lymphopenia, and congenital heart disease. For the rest of the diagnoses, accuracy stayed between 0.75 and 0.99, AUC 0.46-0.87, Gini 0.07-0.75, and LogLoss 0.09-8.55. See tables and figures.DISCUSSION: Clinicians should remember to consider the negative predictive features together with the positives. We are calling this a proof-of-concept study to continue with our explorations. A good performance is encouraging, and the feature importance might aid feature selection for future endeavors. In the meantime, we can learn from the rules derived by the model and build a user-friendly decision tree to generate differential diagnoses.

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Section: Discussionmentioning

confidence: 99%

Who’s your data? Primary immune deficiency differential diagnosis prediction via machine learning and data mining of the USIDNET registry

Barrera

Guzman

Cascajares³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Shahhosseini et al [24] and Gokalp and Tasci [25] compared the algorithm performance among random search, grid search and Bayesian optimization and concluded that Bayesian optimization is superior. Therefore, Bayesian optimization becomes the top choice for ML researchers to fine-tune hyperparameters [26][27][28]. Du and Gao [26] proposed a Bayesian optimization-based dynamic ensemble model that overcomes the limitation of single model-based methods to provide a dynamic forecast combination for time series data.…”

Section: Introductionmentioning

confidence: 99%

“…Du and Gao [26] proposed a Bayesian optimization-based dynamic ensemble model that overcomes the limitation of single model-based methods to provide a dynamic forecast combination for time series data. Gao et al [27] proposed Bayesian hyperparameter optimization of ensemble learning in disease prediction. Nishio et al [28] performed computer-aid diagnosis of lung nodule by the Bayesian optimization-based gradient tree boosting method.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Enabled 3D Body Measurement Estimation Using Hybrid Feature Selection and Bayesian Search

Liu

2022

Applied Sciences

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The 3D body scan technology has recently innovated the way of measuring human bodies and generated a large volume of body measurements. However, one inherent issue that plagues the use of the resultant database is the missing data usually caused by using automatic data extractions from the 3D body scans. Tedious extra efforts have to be made to manually fill the missing data for various applications. To tackle this problem, this paper proposes a machine learning (ML)-based approach for 3D body measurement estimation while considering the measurement (feature) importance. The proposed approach selects the most critical features to reduce the algorithm input and to improve the ML method performance. In addition, a Bayesian search is further used in fine-tuning the hyperparameters to minimize the mean square error. Two distinct ML methods, i.e., Random Forest and XGBoost, are used and tested on a real-world dataset that contains 3D body scans of 212 participants in the Kansas-Missouri area of the United States. The results show the effectiveness of the proposed methods with roughly 3% of Mean Absolute Percentage Errors in estimating the missing data. The two ML methods with the proposed hybrid feature selection and the Baysian search are comprehensively compared. The comparative results suggest that the Random Forest method performs better than the XGBoost counterpart in filling missing 3D body measurements.

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“…Technology based on RL can fuse multi-party data information, and after comprehensive processing, it gives a more reasonable resource utilization and emergency treatment plan, so it is favored by research scholars (Luo et al , 2020; Yan et al , 2020). According to relevant survey data, the RL-based technology can increase the success rate of traffic rescue emergency handling events from 28% to 87% (Jackson et al , 2020; Gao and Ding, 2020). Therefore, its application study in traffic accidents has great positive significance.…”

Section: Introductionmentioning

confidence: 99%

A new path plan method based on hybrid algorithm of reinforcement learning and particle swarm optimization

Liu

Zhang

et al. 2021

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PurposeTo solve the path planning problem of the intelligent driving vehicular, this paper designs a hybrid path planning algorithm based on optimized reinforcement learning (RL) and improved particle swarm optimization (PSO).Design/methodology/approachFirst, the authors optimized the hyper-parameters of RL to make it converge quickly and learn more efficiently. Then the authors designed a pre-set operation for PSO to reduce the calculation of invalid particles. Finally, the authors proposed a correction variable that can be obtained from the cumulative reward of RL; this revises the fitness of the individual optimal particle and global optimal position of PSO to achieve an efficient path planning result. The authors also designed a selection parameter system to help to select the optimal path.FindingsSimulation analysis and experimental test results proved that the proposed algorithm has advantages in terms of practicability and efficiency. This research also foreshadows the research prospects of RL in path planning, which is also the authors’ next research direction.Originality/valueThe authors designed a pre-set operation to reduce the participation of invalid particles in the calculation in PSO. And then, the authors designed a method to optimize hyper-parameters to improve learning efficiency of RL. And then they used RL trained PSO to plan path. The authors also proposed an optimal path evaluation system. This research also foreshadows the research prospects of RL in path planning, which is also the authors’ next research direction.

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Disease prediction via Bayesian hyperparameter optimization and ensemble learning

Cited by 28 publications

References 22 publications

Who’s your data? Primary immune deficiency differential diagnosis prediction via machine learning and data mining of the USIDNET registry

Who’s your data? Primary immune deficiency differential diagnosis prediction via machine learning and data mining of the USIDNET registry

Machine Learning Enabled 3D Body Measurement Estimation Using Hybrid Feature Selection and Bayesian Search

A new path plan method based on hybrid algorithm of reinforcement learning and particle swarm optimization

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