Detection of Schistosomiasis Factors Using Association Rule Mining

Ali, Yasir Salih; Farooq, Amjad; Alam, Talha Mahboob; Farooq, Muhammad Shoaib; Awan, Mazhar Javed; Baig, Talha Imtiaz

doi:10.1109/access.2019.2956020

Cited by 53 publications

(29 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Samplin g will cause the imbalance ratio of the dataset to ch an ge. As IR becomes larger, the disparity in sample size between the majority class and the minority class becomes more significant [98,99]. The dataset at this time is imbalanced .…”

Section: F Evaluation 1) Evaluate Sampling -Imbalance Ratiomentioning

confidence: 99%

A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data

et al. 2021

Self Cite

View full text Add to dashboard Cite

Medical datasets are usually imbalanced, where negative cases severely outnumber p osit iv e cases. Therefore, it is essential to deal with this data skew problem when training machine learning algorithms. This study uses two representative lung cancer datasets, PLCO an d NLST, wit h imb alan ce ratios (the proportion of samples in the majority class to those in the minority class) of 24.7 and 25.0, respectively, to predict lung cancer incidence. This research uses the performance o f 23 clas s imb alan ce methods (resampling and hybrid systems) with three classical classifiers (logistic regression, random forest, and LinearSVC) to identify the best imbalance techniques suitable for medical datasets. Resampling includes ten under-sampling methods (RUS, Etc.), seven over-sampling methods (SMOTE, Etc.), an d t wo integrated sampling methods (SMOTEENN, SMOTE-Tomek). Hybrid systems include (Balanced Bagging, Etc.). The results show that class imbalance learning can improve the classification abilit y o f t h e mo d el. Compared with other imbalanced techniques, under-sampling techniques have the highest standard deviation (SD), and over-sampling techniques have the lowest SD. Over-sampling is a stable met h od, an d the AUC in the model is generally higher than in other ways. Using ROS, the random forest p erforms t h e best predictive ability and is more suitable for the lung cancer datasets used in this study.

show abstract

Section: F Evaluation 1) Evaluate Sampling -Imbalance Ratiomentioning

confidence: 99%

A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…The experimental results review the effect of various degrees of the credit-related imbalanced datasets for training on credit card default prediction model. Various machine learning models were also deployed in the domain of cyber security [46,47], healthcare [48,49], education [50,51] The most efficient results have been obtained through Taiwan's client credit dataset. So the learned weights of that dataset have been employed for the deployment of the model One of the principal objectives in building a model that precisely predicts results and is robust to changes in future information.…”

Section: Deploymentmentioning

confidence: 99%

An Investigation of Credit Card Default Prediction in the Imbalanced Datasets

et al. 2020

Self Cite

View full text Add to dashboard Cite

Financial threats are displaying a trend about the credit risk of commercial banks as the incredible improvement in the financial industry has arisen. In this way, one of the biggest threats faces by commercial banks is the risk prediction of credit clients. Recent studies mostly focus on enhancing the classifier performance for credit card default prediction rather than an interpretable model. In classification problems, an imbalanced dataset is also crucial to improve the performance of the model because most of the cases lied in one class, and only a few examples are in other categories. Traditional statistical approaches are not suitable to deal with imbalanced data. In this study, a model is developed for credit default prediction by employing various credit-related datasets. There is often a significant difference between the minimum and maximum values in different features, so Min-Max normalization is used to scale the features within one range. Data level resampling techniques are employed to overcome the problem of the data imbalance. Various undersampling and oversampling methods are used to resolve the issue of class imbalance. Different machine learning models are also employed to obtain efficient results. We developed the hypothesis of whether developed models using different machine learning techniques are significantly the same or different and whether resampling techniques significantly improves the performance of the proposed models. Oneway Analysis of Variance is a hypothesis-testing technique, used to test the significance of the results. The split method is utilized to validate the results in which data has split into training and test sets. The results on imbalanced datasets show the accuracy of 66.9% on Taiwan clients credit dataset, 70.7% on South German clients credit dataset, and 65% on Belgium clients credit dataset. Conversely, the results using our proposed methods significantly improve the accuracy of 89% on Taiwan clients credit dataset, 84.6% on South German clients credit dataset, and 87.1% on Belgium clients credit dataset. The results show that the performance of classifiers is better on the balanced dataset as compared to the imbalanced dataset. It is also observed that the performance of data oversampling techniques are better than undersampling techniques. Overall, the Gradient Boosted Decision Tree method performs better than other traditional machine learning classifiers. The Gradient Boosted Decision Tree method gives the best results while utilizing the K-means SMOTE oversampling method. Using one-way ANOVA, the null hypothesis was rejected by a p-value <0.001, hence confirming that the proposed model improved performance is statistical significance. The interpretable model is also deployed on the web to ease the different stakeholders. This model will help commercial banks, financial organizations, loan institutes, and other decision-makers to predict the loan defaulter earlier.

show abstract

“…So, it is more efficient than offline augmentation because it does not require extensive training. The technique of offline data augmentation significantly increased the diversity of their available data without actually collecting new data by cropping, padding, flipping, rotating and combining in the case of Alzheimer’s stage detection, brain tumor and others in the MRI [ 52 , 53 , 54 ].…”

Section: Methodsmentioning

confidence: 99%

Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach

et al. 2021

View full text Add to dashboard Cite

The most commonly injured ligament in the human body is an anterior cruciate ligament (ACL). ACL injury is standard among the football, basketball and soccer players. The study aims to detect anterior cruciate ligament injury in an early stage via efficient and thorough automatic magnetic resonance imaging without involving radiologists, through a deep learning method. The proposed approach in this paper used a customized 14 layers ResNet-14 architecture of convolutional neural network (CNN) with six different directions by using class balancing and data augmentation. The performance was evaluated using accuracy, sensitivity, specificity, precision and F1 score of our customized ResNet-14 deep learning architecture with hybrid class balancing and real-time data augmentation after 5-fold cross-validation, with results of 0.920%, 0.916%, 0.946%, 0.916% and 0.923%, respectively. For our proposed ResNet-14 CNN the average area under curves (AUCs) for healthy tear, partial tear and fully ruptured tear had results of 0.980%, 0.970%, and 0.999%, respectively. The proposing diagnostic results indicated that our model could be used to detect automatically and evaluate ACL injuries in athletes using the proposed deep-learning approach.

show abstract

Detection of Schistosomiasis Factors Using Association Rule Mining

Cited by 53 publications

References 15 publications

A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data

A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data

An Investigation of Credit Card Default Prediction in the Imbalanced Datasets

Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach

Contact Info

Product

Resources

About