Prediction of heavy metal removal performance of sulfate-reducing bacteria using machine learning

A robust modeling approach for predicting heavy metal removal by sulfate-reducing bacteria (SRB) is currently missing. In this study, four machine learning models were constructed and compared to predict the removal of Cd, Cu, Pb, and Zn as individual ions by SRB. The CatBoost model exhibited the best predictive performance across the four subsets, achieving R2 values of 0.83, 0.91, 0.92, and 0.83 for the Cd, Cu, Pb, and Zn models, respectively. Feature analysis revealed that temperature, pH, sulfate concentration, and C/S (the mass ratio of chemical oxygen demand to sulfate) had significant impacts on the outcomes. These features exhibited the most effective metal removal at 35 °C and sulfate concentrations of 1000-1200 mg/L, with variations observed in pH and C/S ratios. This study introduced a new modeling approach for predicting the treatment of metal-containing wastewater by SRB, offering guidance for optimizing operational parameters in the biological sulfidogenic process.

Predicting diabetic kidney disease for type 2 diabetes mellitus by machine learning in the real world: a multicenter retrospective study

Objective

Diabetic kidney disease (DKD) has been reported as a main microvascular complication of diabetes mellitus. Although renal biopsy is capable of distinguishing DKD from Non Diabetic kidney disease(NDKD), no gold standard has been validated to assess the development of DKD.This study aimed to build an auxiliary diagnosis model for type 2 Diabetic kidney disease (T2DKD) based on machine learning algorithms.

Methods

Clinical data on 3624 individuals with type 2 diabetes (T2DM) was gathered from January 1, 2019 to December 31, 2019 using a multi-center retrospective database. The data fell into a training set and a validation set at random at a ratio of 8:2. To identify critical clinical variables, the absolute shrinkage and selection operator with the lowest number was employed. Fifteen machine learning models were built to support the diagnosis of T2DKD, and the optimal model was selected in accordance with the area under the receiver operating characteristic curve (AUC) and accuracy. The model was improved with the use of Bayesian Optimization methods. The Shapley Additive explanations (SHAP) approach was used to illustrate prediction findings.

Results

DKD was diagnosed in 1856 (51.2 percent) of the 3624 individuals within the final cohort. As revealed by the SHAP findings, the Categorical Boosting (CatBoost) model achieved the optimal performance 1in the prediction of the risk of T2DKD, with an AUC of 0.86 based on the top 38 characteristics. The SHAP findings suggested that a simplified CatBoost model with an AUC of 0.84 was built in accordance with the top 12 characteristics. The more basic model features consisted of systolic blood pressure (SBP), creatinine (CREA), length of stay (LOS), thrombin time (TT), Age, prothrombin time (PT), platelet large cell ratio (P-LCR), albumin (ALB), glucose (GLU), fibrinogen (FIB-C), red blood cell distribution width-standard deviation (RDW-SD), as well as hemoglobin A1C(HbA1C).

Conclusion

A machine learning-based model for the prediction of the risk of developing T2DKD was built, and its effectiveness was verified. The CatBoost model can contribute to the diagnosis of T2DKD. Clinicians could gain more insights into the outcomes if the ML model is made interpretable.

CatBoost-based improved detection of P-wave changes in sinus rhythm and tachycardia conditions: a lead selection study

Examining P-wave morphological changes in Electrocardiogram (ECG) is essential for characterizing atrial arrhythmias. However, standard 12-lead ECGsuffer from diagnostic redundancy due to low signal-to-noise ratio of P-waves. To address this issue, various optimal leads have been proposed for improved atrial activity recording, but the right selection among these leads is crucial for enhancing diagnostic efficacy. This study proposes an automated lead selection technique using the CatBoost machine learning (ML) model to improve the detection of P-wave changes among optimal bipolar leads under different heart rates. ECGs were obtained from healthy participants with a mean age of 25 ± 3.81 years (34% women), including 114 in sinus rhythm (SR) and 38 in sinus tachycardia (ST). The recordings were made using a newly designed atrial lead system (ALS), standard limb lead (SLL), modified limb lead (MLL), modified Lewis lead (LLM) and P-lead. P-wave features and Atrioventricular (AV) ratio were extracted for statistical analysis and ML classification. The optimum ML model was chosen to identify the best-performing optimal lead, which was selected based on the SLL metrics among different ML classifiers. CatBoost was found to outperform the other ML models in SLL-II with the highest accuracy and sensitivity of 0.82 and 0.90, respectively. The CatBoost model, amid other optimal leads, gave the best results for AL-I and AL-II (0.86 and 0.83 in accuracy and 0.91 and 0.93 in sensitivity). The developed CatBoost model selected AL-I and AL-II as the top two best-performing optimal leads for the enhanced acquisition of P-wave changes, which may be useful for diagnosing atrial arrhythmias.

Applying Machine Learning to Carotid Sonographic Features for Recurrent Stroke in Patients With Acute Stroke

BACKGROUND

Although carotid sonographic features have been used as predictors of recurrent stroke, few large-scale studies have explored the use of machine learning analysis of carotid sonographic features for the prediction of recurrent stroke.

METHODS

We retrospectively collected electronic medical records of enrolled patients from the data warehouse of China Medical University Hospital, a tertiary medical center in central Taiwan, from January 2012 to November 2018. We included patients who underwent a documented carotid ultrasound within 30 days of experiencing an acute first stroke during the study period. We classified these participants into two groups: those with non-recurrent stroke (those who has not been diagnosed with acute stroke again during the study period) and those with recurrent stoke (those who has been diagnosed with acute stroke during the study period). A total of 1,235 carotid sonographic parameters were analyzed. Data on the patients' demographic characteristics and comorbidities were also collected. Python 3.7 was used as the programming language, and the scikit-learn toolkit was used to complete the derivation and verification of the machine learning methods.

RESULTS

In total, 2,411 patients were enrolled in this study, of whom 1,896 and 515 had non-recurrent and recurrent stroke, respectively. After extraction, 43 features of carotid sonography (36 carotid sonographic parameters and seven transcranial color Doppler sonographic parameter) were analyzed. For predicting recurrent stroke, CatBoost achieved the highest area under the curve (0.844, CIs 95% 0.824-0.868), followed by the Light Gradient Boosting Machine (0.832, CIs 95% 0.813-0.851), random forest (0.819, CIs 95% 0.802-0.846), support-vector machine (0.759, CIs 95% 0.739-0.781), logistic regression (0.781, CIs 95% 0.764-0.800), and decision tree (0.735, CIs 95% 0.717-0.755) models.

CONCLUSION

When using the CatBoost model, the top three features for predicting recurrent stroke were determined to be the use of anticoagulation medications, the use of NSAID medications, and the resistive index of the left subclavian artery. The CatBoost model demonstrated efficiency and achieved optimal performance in the predictive classification of non-recurrent and recurrent stroke.