SKNN Algorithm for Filling Missing Oil Data Based on KNN

Wang, Mei; Dong, Li; Qi, Kaiyuan; Xue, Chao; Yang, Erlong

doi:10.1088/1757-899x/612/3/032099

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…The KNN lling algorithm is a local single lling algorithm, which uses the attribute values corresponding to K-nearest neighbor samples to determine the missing attribute values of the missing samples. It can solve the problem of missing data simply and quickly [9] . We discovered the situation of missing values after detecting 813 items of original data in this study, plotted the corresponding feature variables into a histogram (see Fig.…”

Section: Data Preprocessingmentioning

confidence: 99%

An ACS-Stacking Prediction Model Based on Interpretable Machine Learning

Wang,

Liu,

Wang

2024

Preprint

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Background: Acute coronary syndrome (ACS) is an important disease threatening human health, and the rapid differential diagnosis of acute myocardial infarction still requires further studies. Purpose: This study aims to establish an interpretable machine learning (ML) model and perform visual and interpretable analysis to the prediction results using SHAP (SHapley Additive exPlanation). Then significant correlation indicators are determined to assist clinicians in providing rapid and effective identification for ACS patients. Method: This study involves the clinical data of 813 patients from the Shanxi Cardiovascular Hospital , which is described by 24 predictor variables in relation to demography/comorbidity characteristics and in-hospital complications. Taking the binary variables of “Acute Myocardial Infarction (AMI) and Unstable Angina (UA)” as target variables, we have trained and evaluated the performance of seven ML models in this study and fused Adaboost, Xgboost and Randomforest with better performance in the test set into the best interpretable Stacking fusion model (named as: ACS-Stacking prediction model). Results: The ACS fusion prediction model achieves an AUC value of 0.96562 in the test set and an accuracy of 89% under 10-fold cross-validation. This study interprets the model using SHAP. Among the related continuous variables, neutrophil and admission heart rate have a positive effect on the mode while LVEF, BMI, systolic pressure and diastolic pressure have a negative effect on the model. However, age is not significantly correlated with target variables. For the classified variables, the patients with smoking history are predisposed to myocardial infarction; sex and history of hypertension are not significantly correlated with target variables. Conclusion: This study shows that the interpretable ACS-Stacking prediction model has a good differential prediction effect on myocardial infarction and angina pectoris, and the Summary Plot shows the specific effect of ten significant correlation indicators on the output of the model. This conclusion helps clinicians to rapidly identify ACS patients in clinical diagnosis based on the model prediction result, model visualization and clinical experience.

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Section: Data Preprocessingmentioning

confidence: 99%

An ACS-Stacking Prediction Model Based on Interpretable Machine Learning

Wang,

Liu,

Wang

2024

Preprint

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“…In this paper, we introduce a padding method using missing values of adjacent data points -K-Nearest Neighbor algorithm [4][5][6], which identifies adjacent points by measuring the distance between points, estimates and fills missing values, and is robust to noise.…”

Section: Data Processingmentioning

confidence: 99%

Prediction and Key Characteristics of All-Cause Mortality in Maintenance Hemodialysis Patients

Mu¹,

Zhu²,

Liu³

et al. 2022

Signal &Amp; Image Processing Trends

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Predict and analyze key features of all-cause death in maintenance hemodialysis patients to provide guidance for later diagnosis and treatment. Four machine learning methods were used to establish an all-cause death prediction model for maintenance hemodialysis patients and compare their performance. Analyze the key characteristics that have an important impact on all-cause death, and conduct user portraits for patients of different ages and genders. After comparison, the random forest algorithm works best, and an important factor affecting the all-cause death of patients is obtained. Among them, the all-cause death of all patients is related to factors such as albumin, blood potassium, blood magnesium, and urea; With age, the importance of factors such as blood sodium and phosphorus increases, and the importance of factors such as cardiac ultrasound ejection fraction decreases. Finally, there were also differences in the importance of analyzing patients of different ages and different sexes affecting their all-cause death. It is useful for residents to adjust their dialysis index timely.

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A missing value filling model based on feature fusion enhanced autoencoder

Liu,

Du,

et al. 2023

Appl Intell

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SKNN Algorithm for Filling Missing Oil Data Based on KNN

Cited by 4 publications

References 4 publications

An ACS-Stacking Prediction Model Based on Interpretable Machine Learning

An ACS-Stacking Prediction Model Based on Interpretable Machine Learning

Prediction and Key Characteristics of All-Cause Mortality in Maintenance Hemodialysis Patients

A missing value filling model based on feature fusion enhanced autoencoder

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