RETRACTED ARTICLE: An investigation on the risk awareness model and the economic development of the financial sector

Wang, Gang; Wang, Lei; Mohammed, Badamasi Sani; Hamad, Àbdulsattar Abdullah

doi:10.1007/s10479-021-04517-y

Cited by 9 publications

(9 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The impact that is desired to obtain with the project in applying decision and regression trees as a tool for the prognosis of medical conditions is to take optimal management of the WEKA software [18]. The classification trees are the most competent for these data, more precisely the logistic model tree or LMT [19] classification tree, which statistically turned out to be the type of tree that presented the most efficient results in its result statistics with an average of 80% correct classifications at the time of executing on the data, whose response or interest variables were the Tymp() variable and the speech() variable, which correspond to the type of eardrum and if the person has problems of speaking [7,20,21].…”

Section: Discussionmentioning

confidence: 99%

A Comparison of Decision Tree Algorithms in the Assessment of Biomedical Data

Hajjej

Alohali

Badr³

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

By comparing the performance of various tree algorithms, we can determine which one is most useful for analyzing biomedical data. In artificial intelligence, decision trees are a classification model known for their visual aid in making decisions. WEKA software will evaluate biological data from real patients to see how well the decision tree classification algorithm performs. Another goal of this comparison is to assess whether or not decision trees can serve as an effective tool for medical diagnosis in general. In doing so, we will be able to see which algorithms are the most efficient and appropriate to use when delving into this data and arrive at an informed decision.

show abstract

Section: Discussionmentioning

confidence: 99%

A Comparison of Decision Tree Algorithms in the Assessment of Biomedical Data

Hajjej

Alohali

Badr³

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…This method employs many trees instead of a decision tree. A random vector determines the value of each tree in the forest [ 12 , 13 ]. The number of trees can be planned.…”

Section: Methodsmentioning

confidence: 99%

“…Boosting a weak learning algorithm by majority [ 13 ] creates a strong algorithm from a linear combination of weak algorithms. The fact that these weak algorithms outperform random algorithms is enough to use them.…”

Section: Methodsmentioning

confidence: 99%

Diagnosing Cancer Using IOT and Machine Learning Methods

Maray

Alghamdi

Alazzam

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Breast cancer affects one in every eight women and is the most common cancer. Aim. To diagnose breast cancer, a potentially fatal condition, using microarray technology, large datasets can now be used. Methods. This study used machine learning algorithms and IOT to classify microarray data. They were created from two sets of data: one with 1919 protein types and one with 24481 protein types for 97 people, 46 of whom had a recurring disease and 51 of whom did not. The apps were written in Python. Each classification algorithm was applied to the data separately, without any feature elimination or size reduction. Second, two alternative feature reduction approaches were compared to the first case. In this case, machine learning techniques like Adaboost and Gradient Boosting Machine are used. Results. Before applying any feature reduction techniques, the logistic regression method produced the best results (90.23%), while the Random Forest method produced good results (67.22%). In the first data, SVM had the highest accuracy rate of 99.23% in both approaches, while in the second data, SVM had the highest rate of 87.87% in RLR and 88.82% in LTE. Deep learning was also done with MLP. The relationship between depth and classification accuracy was studied using it at various depths. After a while, the accuracy rate declined as the number of layers increased. The maximum accuracy rate in the first data was 97.69%, while it was 68.72% in the second. As a result, adding layers to deep learning does not improve classification accuracy.

show abstract

“…Neighbor (KNN). One of the supervised learning strategies for solving the classification problem is the K-nearest neighbor method [18,19]. By assessing the similarity of the data to be classified in the technique to the normal behavior data in the learning set, they are assigned to the classes based on the specified threshold value.…”

Section: K-nearestmentioning

confidence: 99%

Diagnosis of Breast Cancer Pathology on the Wisconsin Dataset with the Help of Data Mining Classification and Clustering Techniques

Mohammad

Teete

Al-Aaraj

et al. 2022

Applied Bionics and Biomechanics

View full text Add to dashboard Cite

Breast cancer must be addressed by a multidisciplinary team aiming at the patient’s comprehensive treatment. Recent advances in science make it possible to evaluate tumor staging and point out the specific treatment. However, these advances must be combined with the availability of resources and the easy operability of the technique. This study is aimed at distinguishing and classifying benign and malignant cells, which are tumor types, from the data on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset by applying data mining classification and clustering techniques with the help of the Weka tool. In addition, various algorithms and techniques used in data mining were measured with success percentages, and the most successful ones on the dataset were determined and compared with each other.

show abstract

RETRACTED ARTICLE: An investigation on the risk awareness model and the economic development of the financial sector

Cited by 9 publications

References 24 publications

A Comparison of Decision Tree Algorithms in the Assessment of Biomedical Data

A Comparison of Decision Tree Algorithms in the Assessment of Biomedical Data

Diagnosing Cancer Using IOT and Machine Learning Methods

Diagnosis of Breast Cancer Pathology on the Wisconsin Dataset with the Help of Data Mining Classification and Clustering Techniques

Contact Info

Product

Resources

About