scholarly journals TESTING PREDICTION ACCURACY OF IDEAL AND PROLONGED LENGTH OF HOSPITAL STAY FOLLOWING OVARIAN CANCER CYTOREDUCTION USING MACHINE LEARNING METHODS.

2020 ◽  
Author(s):  
Alexandros Laios
Keyword(s):  
Machine Learning ◽  
Ovarian Cancer ◽  
Hospital Stay ◽  
Prediction Accuracy ◽  
Length Of Hospital Stay ◽  
Learning Methods ◽  
Prolonged Length ◽  
Machine Learning Methods

Predictions of chalcospinels with composition ABCX4 (X – S or Se)

2020 ◽  
pp. 5-18
Author(s):  
N. N. Kiselyova ◽  
◽  
V. A. Dudarev ◽  
V. V. Ryazanov ◽  
O. V. Sen’ko ◽  
...  
Keyword(s):  
Machine Learning ◽  
Crystal Lattice ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Chemical Elements ◽  
Optical Memory ◽  
Support Vector ◽  
Learning Methods ◽  
Linear Discriminant ◽  
Machine Learning Methods

New chalcospinels of the most common compositions were predicted: AIBIIICIVX4 (X — S or Se) and AIIBIIICIIIS4 (A, B, and C are various chemical elements). They are promising for the search for new materials for magneto-optical memory elements, sensors and anodes in sodium-ion batteries. The parameter “a” values of their crystal lattice are estimated. When predicting only the values of chemical elements properties were used. The calculations were carried out using machine learning programs that are part of the information-analytical system developed by the authors (various ensembles of algorithms of: the binary decision trees, the linear machine, the search for logical regularities of classes, the support vector machine, Fisher linear discriminant, the k-nearest neighbors, the learning a multilayer perceptron and a neural network), — for predicting chalcospinels not yet obtained, as well as an extensive family of regression methods, presented in the scikit-learn package for the Python language, and multilevel machine learning methods that were proposed by the authors — for estimation of the new chalcospinels lattice parameter value). The prediction accuracy of new chalcospinels according to the results of the cross-validation is not lower than 80%, and the prediction accuracy of the parameter of their crystal lattice (according to the results of calculating the mean absolute error (when cross-validation in the leave-one-out mode)) is ± 0.1 Å. The effectiveness of using multilevel machine learning methods to predict the physical properties of substances was shown.

STACKING OF THE SGTM NEURAL-LIKE STRUCTURE WITH RBF LAYER BASED ON GENERATION OF A RANDOM CURTAIN OF ITS HYPERPARAMETERS FOR PREDICTION TASKS

2021 ◽  
Vol 3 (1) ◽  
pp. 49-55
Author(s):  
R. O. Tkachenko ◽  
◽  
I. V. Izonіn ◽  
V. M. Danylyk ◽  
V. Yu. Mykhalevych ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Experimental Studies ◽  
Real Data ◽  
Optimal Number ◽  
Individual Member ◽  
Optimal Parameters ◽  
Learning Methods ◽  
Data Set ◽  
Machine Learning Methods

Improving prediction accuracy by artificial intelligence tools is an important task in various industries, economics, medicine. Ensemble learning is one of the possible options to solve this task. In particular, the construction of stacking models based on different machine learning methods, or using different parts of the existing data set demonstrates high prediction accuracy of the. However, the need for proper selection of ensemble members, their optimal parameters, etc., necessitates large time costs for the construction of such models. This paper proposes a slightly different approach to building a simple but effective ensemble method. The authors developed a new model of stacking of nonlinear SGTM neural-like structures, which is based on the use of only one type of ANN as an element base of the ensemble and the use of the same training sample for all members of the ensemble. This approach provides a number of advantages over the procedures for building ensembles based on different machine learning methods, at least in the direction of selecting the optimal parameters for each of them. In our case, a tuple of random hyperparameters for each individual member of the ensemble was used as the basis of ensemble. That is, the training of each combined SGTM neural-like structure with an additional RBF layer, as a separate member of the ensemble occurs using different, randomly selected values of RBF centers and centersfof mass. This provides the necessary variety of ensemble elements. Experimental studies on the effectiveness of the developed ensemble were conducted using a real data set. The task is to predict the amount of health insurance costs based on a number of independent attributes. The optimal number of ensemble members is determined experimentally, which provides the highest prediction accuracy. The results of the work of the developed ensemble are compared with the existing methods of this class. The highest prediction accuracy of the developed ensemble at satisfactory duration of procedure of its training is established.

scholarly journals Defining and Predicting Pain Volatility in Users of the Manage My Pain App: Analysis Using Data Mining and Machine Learning Methods (Preprint)

2018 ◽  
Author(s):  
Quazi Abidur Rahman ◽  
Tahir Janmohamed ◽  
Meysam Pirbaglou ◽  
Hance Clarke ◽  
Paul Ritvo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Random Forests ◽  
Prediction Accuracy ◽  
Clustering Algorithm ◽  
Prediction Models ◽  
Class Imbalance ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
High Volatility

BACKGROUND Measuring and predicting pain volatility (fluctuation or variability in pain scores over time) can help improve pain management. Perceptions of pain and its consequent disabling effects are often heightened under the conditions of greater uncertainty and unpredictability associated with pain volatility. OBJECTIVE This study aimed to use data mining and machine learning methods to (1) define a new measure of pain volatility and (2) predict future pain volatility levels from users of the pain management app, Manage My Pain, based on demographic, clinical, and app use features. METHODS Pain volatility was defined as the mean of absolute changes between 2 consecutive self-reported pain severity scores within the observation periods. The k-means clustering algorithm was applied to users’ pain volatility scores at the first and sixth month of app use to establish a threshold discriminating low from high volatility classes. Subsequently, we extracted 130 demographic, clinical, and app usage features from the first month of app use to predict these 2 volatility classes at the sixth month of app use. Prediction models were developed using 4 methods: (1) logistic regression with ridge estimators; (2) logistic regression with Least Absolute Shrinkage and Selection Operator; (3) Random Forests; and (4) Support Vector Machines. Overall prediction accuracy and accuracy for both classes were calculated to compare the performance of the prediction models. Training and testing were conducted using 5-fold cross validation. A class imbalance issue was addressed using a random subsampling of the training dataset. Users with at least five pain records in both the predictor and outcome periods (N=782 users) are included in the analysis. RESULTS k-means clustering algorithm was applied to pain volatility scores to establish a threshold of 1.6 to differentiate between low and high volatility classes. After validating the threshold using random subsamples, 2 classes were created: low volatility (n=611) and high volatility (n=171). In this class-imbalanced dataset, all 4 prediction models achieved 78.1% (611/782) to 79.0% (618/782) in overall accuracy. However, all models have a prediction accuracy of less than 18.7% (32/171) for the high volatility class. After addressing the class imbalance issue using random subsampling, results improved across all models for the high volatility class to greater than 59.6% (102/171). The prediction model based on Random Forests performs the best as it consistently achieves approximately 70% accuracy for both classes across 3 random subsamples. CONCLUSIONS We propose a novel method for measuring pain volatility. Cluster analysis was applied to divide users into subsets of low and high volatility classes. These classes were then predicted at the sixth month of app use with an acceptable degree of accuracy using machine learning methods based on the features extracted from demographic, clinical, and app use information from the first month.

scholarly journals Clinical Predictors of Prolonged Hospital Stay in Patients with Myasthenia Gravis: A Study Using Machine Learning Algorithms

2021 ◽  
Vol 10 (19) ◽  
pp. 4393
Author(s):  
Che-Cheng Chang ◽  
Jiann-Horng Yeh ◽  
Yen-Ming Chen ◽  
Mao-Jhen Jhou ◽  
Chi-Jie Lu
Keyword(s):  
Machine Learning ◽  
Myasthenia Gravis ◽  
Hospital Stay ◽  
Gradient Boosting ◽  
High Dose ◽  
Prolonged Hospital Stay ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Prolonged Hospitalization ◽  
Prolonged Hospital

Myasthenia gravis (MG) is an autoimmune disorder that causes muscle weakness. Although the management is well established, some patients are refractory and require prolonged hospitalization. Our study is aimed to identify the important factors that predict the duration of hospitalization in patients with MG by using machine learning methods. A total of 21 factors were chosen for machine learning analyses. We retrospectively reviewed the data of patients with MG who were admitted to hospital. Five machine learning methods, including stochastic gradient boosting (SGB), least absolute shrinkage and selection operator (Lasso), ridge regression (Ridge), eXtreme gradient boosting (XGboost), and gradient boosting with categorical features support (Catboost), were used to construct models for identify the important factors affecting the duration of hospital stay. A total of 232 data points of 204 hospitalized MG patients admitted were enrolled into the study. The MGFA classification, treatment of high-dose intravenous corticosteroid, age at admission, treatment with intravenous immunoglobulins, and thymoma were the top five significant variables affecting prolonged hospitalization. Our findings from machine learning will provide physicians with information to evaluate the potential risk of MG patients having prolonged hospital stay. The use of high-dose corticosteroids is associated with prolonged hospital stay and to be used cautiously in MG patients.

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