Implementation of Machine Learning Models for the Prevention of Kidney Diseases (CKD) or Their Derivatives

Chronic kidney disease (CKD) is a global health issue with a high rate of morbidity and mortality and a high rate of disease progression. Because there are no visible symptoms in the early stages of CKD, patients frequently go unnoticed. The early detection of CKD allows patients to receive timely treatment, slowing the disease’s progression. Due to its rapid recognition performance and accuracy, machine learning models can effectively assist physicians in achieving this goal. We propose a machine learning methodology for the CKD diagnosis in this paper. This information was completely anonymized. As a reference, the CRISP-DM® model (Cross industry standard process for data mining) was used. The data were processed in its entirety in the cloud on the Azure platform, where the sample data was unbalanced. Then the processes for exploration and analysis were carried out. According to what we have learned, the data were balanced using the SMOTE technique. Four matching algorithms were used after the data balancing was completed successfully. Artificial intelligence (AI) (logistic regression, decision forest, neural network, and jungle of decisions). The decision forest outperformed the other machine learning models with a score of 92%, indicating that the approach used in this study provides a good baseline for solutions in the production.

Prediction of Tissue Damage Using a User-Independent Machine Learning Algorithm vs. Tmax Threshold Maps

(1) Background: To test the accuracy of a fully automated stroke tissue estimation algorithm (FASTER) to predict final lesion volumes in an independent dataset in patients with acute stroke; (2) Methods: Tissue-at-risk prediction was performed in 31 stroke patients presenting with a proximal middle cerebral artery occlusion. FDA-cleared perfusion software using the AHA recommendation for the Tmax threshold delay was tested against a prediction algorithm trained on an independent perfusion software using artificial intelligence (FASTER). Following our endovascular strategy to consequently achieve TICI 3 outcome, we compared patients with complete reperfusion (TICI 3) vs. no reperfusion (TICI 0) after mechanical thrombectomy. Final infarct volume was determined on a routine follow-up MRI or CT at 90 days after the stroke; (3) Results: Compared to the reference standard (infarct volume after 90 days), the decision forest algorithm overestimated the final infarct volume in patients without reperfusion. Underestimation was observed if patients were completely reperfused. In cases where the FDA-cleared segmentation was not interpretable due to improper definitions of the arterial input function, the decision forest provided reliable results; (4) Conclusions: The prediction accuracy of automated tissue estimation depends on (i) success of reperfusion, (ii) infarct size, and (iii) software-related factors introduced by the training sample. A principal advantage of machine learning algorithms is their improved robustness to artifacts in comparison to solely threshold-based model-dependent software. Validation on independent datasets remains a crucial condition for clinical implementations of decision support systems in stroke imaging.

Two-Level Regression Method Using Ensembles of Trees with Optimal Divergence

The article discusses a new two-level regression analysis method in which a corrective procedure is applied to optimal ensembles of regression trees. Optimization is carried out based on the simultaneous achievement of the divergence of the algorithms in the forecast space and a good approximation of the data by individual algorithms of the ensemble. Simple averaging, random regression forest, and gradient boosting are used as corrective procedures. Experiments are presented comparing the proposed method with the standard decision forest and the standard gradient boosting method for decision trees.

Hazard identification and early warning system based on stochastic forest algorithm in underground coal mine

Aiming at the problem of large error and long time of early warning response in the traditional system, this paper designs a hazard identification early warning system based on random forest algorithm in underground coal mine. By random classification decision forest created dangerous content in different areas of the downhole information input into the decision tree as a test sample, according to the result of the output of the leaf node determine the risk level of decision trees, and USES the high precision of decision forest classification ability the threat level assessment test sample, radically reducing hazards identification error. Then, based on the evaluation results, combined with the threshold value of warning criteria to identify the gas exceeding limit area, and determine the fire source warning level, so as to realize the hazard source identification and warning. The simulation results show that the average hazard location identification error of the system is only 4.1%, and the warning response time can be controlled within 9 s.