Predicting Drug Risk Level from Adverse Drug Reactions Using SMOTE and Machine Learning Approaches

Wei, Jianxiang; Zhiqiang, Liang; Qiu, Kai; Li, Pengyang; Sun, Haofei

doi:10.1109/access.2020.3029446

Cited by 22 publications

(8 citation statements)

References 32 publications

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“…For each dataset (Tumor Grade Dataset n � 245 and Overall Survival Dataset n � 150), data were randomly split into training and testing sets with a test size � 0.2, yielding training sets containing 196/120 samples and testing sets containing 49/30 samples, respectively. To prevent the class imbalances from affecting the models' performance, we applied the synthetic minority oversampling (SMOTE) [26] technique to the tumor grade training dataset due to the existing radiomics studies that have shown SMOTE can effectively improve the classification predictive performance when the classes are imbalanced. However, SMOTE was not applied to the Overall Survival Dataset since classes were already balanced.…”

Section: Methodsmentioning

confidence: 99%

Tumor Grade and Overall Survival Prediction of Gliomas Using Radiomics

Huang

Jiang

et al. 2021

Scientific Programming

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Glioma is one of the most common and deadly malignant brain tumors originating from glial cells. For personalized treatment, an accurate preoperative prognosis for glioma patients is highly desired. Recently, various machine learning-based approaches have been developed to predict the prognosis based on preoperative magnetic resonance imaging (MRI) radiomics, which extract quantitative features from radiographic images. However, major challenges remain for methodologic developments to optimize feature extraction and provide rapid information flow in clinical settings. This study investigates two machine learning-based prognosis prediction tasks using radiomic features extracted from preoperative multimodal MRI brain data: (i) prediction of tumor grade (higher-grade vs. lower-grade gliomas) from preoperative MRI scans and (ii) prediction of patient overall survival (OS) in higher-grade gliomas (<12 months vs. > 12 months) from preoperative MRI scans. Specifically, these two tasks utilize the conventional machine learning-based models built with various classifiers. Moreover, feature selection methods are applied to increase model performance and decrease computational costs. In the experiments, models are evaluated in terms of their predictive performance and stability using a bootstrap approach. Experimental results show that classifier choice and feature selection technique plays a significant role in model performance and stability for both tasks; a variability analysis indicates that classification method choice is the most dominant source of performance variation for both tasks.

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Section: Methodsmentioning

confidence: 99%

Tumor Grade and Overall Survival Prediction of Gliomas Using Radiomics

Huang

Jiang

et al. 2021

Scientific Programming

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“…To address the bias problem in the training set, the synthetic minority oversampling technique (SMOTE) [ 38 ], a data augmentation approach that has been widely used in previous research, was applied in this study [ 39 , 40 , 41 , 42 ]. The SMOTE was accomplished by the imbalanced-learn package [ 43 ] (vision 0.8.0).…”

Section: Methodsmentioning

confidence: 99%

Drug-Induced Immune Thrombocytopenia Toxicity Prediction Based on Machine Learning

et al. 2022

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Drug-induced immune thrombocytopenia (DITP) often occurs in patients receiving many drug treatments simultaneously. However, clinicians usually fail to accurately distinguish which drugs can be plausible culprits. Despite significant advances in laboratory-based DITP testing, in vitro experimental assays have been expensive and, in certain cases, cannot provide a timely diagnosis to patients. To address these shortcomings, this paper proposes an efficient machine learning-based method for DITP toxicity prediction. A small dataset consisting of 225 molecules was constructed. The molecules were represented by six fingerprints, three descriptors, and their combinations. Seven classical machine learning-based models were examined to determine an optimal model. The results show that the RDMD + PubChem-k-NN model provides the best prediction performance among all the models, achieving an area under the curve of 76.9% and overall accuracy of 75.6% on the external validation set. The application domain (AD) analysis demonstrates the prediction reliability of the RDMD + PubChem-k-NN model. Five structural fragments related to the DITP toxicity are identified through information gain (IG) method along with fragment frequency analysis. Overall, as far as known, it is the first machine learning-based classification model for recognizing chemicals with DITP toxicity and can be used as an efficient tool in drug design and clinical therapy.

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“…e confusion matrix table is useful to quantify the number of misclassifications for both the negative and positive classes [55]. e total sample size used during testing is the sum of TN, FN, TP, and FP as per the blueprint of the confusion matrix.…”

Section: E Confusion Matrix " E Confusion Matrix Tablementioning

confidence: 99%

“…e prediction score was used to calculate the average precision (AP). At each threshold, the weighted mean of precisions achieved, with the increase in recall from the preceding threshold used as the weight, is how AP summarizes a precision-recall curve [55]:…”

Section: E Precision-recall Curvementioning

confidence: 99%

Solving Misclassification of the Credit Card Imbalance Problem Using Near Miss

Mqadi

Naicker

Adeliyi

2021

Mathematical Problems in Engineering

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In ordinary credit card datasets, there are far fewer fraudulent transactions than ordinary transactions. In dealing with the credit card imbalance problem, the ideal solution must have low bias and low variance. The paper aims to provide an in-depth experimental investigation of the effect of using a hybrid data-point approach to resolve the class misclassification problem in imbalanced credit card datasets. The goal of the research was to use a novel technique to manage unbalanced datasets to improve the effectiveness of machine learning algorithms in detecting fraud or anomalous patterns in huge volumes of financial transaction records where the class distribution was imbalanced. The paper proposed using random forest and a hybrid data-point approach combining feature selection with Near Miss-based undersampling technique. We assessed the proposed method on two imbalanced credit card datasets, namely, the European Credit Card dataset and the UCI Credit Card dataset. The experimental results were reported using performance matrices. We compared the classification results of logistic regression, support vector machine, decision tree, and random forest before and after using our approach. The findings showed that the proposed approach improved the predictive accuracy of the logistic regression, support vector machine, decision tree, and random forest algorithms in credit card datasets. Furthermore, we found that, out of the four algorithms, the random forest produced the best results.

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Predicting Drug Risk Level from Adverse Drug Reactions Using SMOTE and Machine Learning Approaches

Cited by 22 publications

References 32 publications

Tumor Grade and Overall Survival Prediction of Gliomas Using Radiomics

Tumor Grade and Overall Survival Prediction of Gliomas Using Radiomics

Drug-Induced Immune Thrombocytopenia Toxicity Prediction Based on Machine Learning

Solving Misclassification of the Credit Card Imbalance Problem Using Near Miss

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