Comparison of Decision Tree-Based Learning Algorithms Using Breast Cancer Data

Preeclampsia (PE) is a maternal disease that causes maternal and child death. Treatment and preventive measures are not sound enough. The problem of PE screening has attracted much attention. The purpose of this study is to screen placental mRNA to obtain the best PE biomarkers for identifying patients with PE. We use Limma in the R language to screen out the 48 differentially expressed genes with the largest differences and used correlation-based feature selection algorithms to reduce the dimensionality and avoid attribute redundancy arising from too many mRNA samples participating in the classification. After reducing the mRNA attributes, the mRNA samples are sorted from large to small according to information gain. In this study, a classifier model is designed to identify whether samples had PE through mRNA in the placenta. To improve the accuracy of classification and avoid overfitting, three classifiers, including C4.5, AdaBoost, and multilayer perceptron, are used. We use the majority voting strategy integrated with the differentially expressed genes and the genes filtered by the best subset method as comparison methods to train the classifier. The results show that the classification accuracy rate has increased from 79% to 82.2%, and the number of mRNA features has decreased from 48 to 13. This study provides clues for the main PE biomarkers of mRNA in the placenta and provides ideas for the treatment and screening of PE.

Computational and Mathematical Methods in Medicine

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Section: Methodsmentioning

confidence: 99%

Integrated Learning: Screening Optimal Biomarkers for Identifying Preeclampsia in Placental mRNA Samples

Guo

Teng

Wang

et al. 2021

“…The study found that KNN achieved high performance in term of the accuracy as compared to other classi cation algorithms. In a study conducted by Dawngliani et. al (2020) to classify benign and malignant tumors for breast cancer screening dataset, J48 decision tree outperformed other decision tree algorithms in terms of the accuracy.…”

Section: Related Workmentioning

confidence: 99%

Comparison of Classification Algorithms for Predicting Autistic Spectrum Disorder Using WEKA Modeler

Hassan

2021

Preprint

Background: In healthcare area, big data, if integrated with machine learning, enables health practitioners to predict the result of a disorder or disease more accurately. In Autistic Spectrum Disorder (ASD), it is important to screen the patients to enable them to undergo proper treatments as early as possible. However, difficulties may arise in predicting ASD occurrences accurately, mainly caused by human errors. Data mining, if embedded into health screening practice, can help to overcome the difficulties. This study attempts to evaluate the performance of six best classifiers, taken from existing works, at analysing ASD screening training dataset.Result: We tested Naive Bayes, Logistic Regression, KNN, J48, Random Forest, and Deep Neural Network (DNN) algorithms to ASD screening dataset and compared the classifiers’ based on significant parameters; sensitivity, specificity, accuracy, receiver operating characteristic (ROC), area under the curve (AUC), and runtime, in predicting ASD occurrences. We also found that most of previous studies focused on classifying health-related dataset while ignoring the missing values which may contribute to significant impacts to the classification result which in turn will impact the life of the patients. Thus, we addressed the missing values by implementing imputation method where they are replaced with the mean of the available records found in the dataset. Conclusion: We found that J48 produced promising results as compared to other classifiers when tested in both circumstances, with and without missing values. The outcome is hoped to assist health practitioners in making accurate diagnosis of ASD occurrences in patients.

“…The study found that KNN achieved high performance in term of the accuracy as compared to other classification algorithms. In a study conducted by Dawngliani et al [ 17 ] to classify benign and malignant tumors for breast cancer screening dataset, J48 decision tree outperformed other decision tree algorithms in terms of the accuracy. The study tested J48 with other decision tree algorithms namely Decision Stump, Random Forest Tree, REP tree, Hoeffding Tree and Logistic Model Tree (LMT).…”

Section: Introductionmentioning

confidence: 99%

Comparison of classification algorithms for predicting autistic spectrum disorder using WEKA modeler

Hassan

BMC Med Inform Decis Mak

2022

Background In healthcare area, big data, if integrated with machine learning, enables health practitioners to predict the result of a disorder or disease more accurately. In Autistic Spectrum Disorder (ASD), it is important to screen the patients to enable them to undergo proper treatments as early as possible. However, difficulties may arise in predicting ASD occurrences accurately, mainly caused by human errors. Data mining, if embedded into health screening practice, can help to overcome the difficulties. This study attempts to evaluate the performance of six best classifiers, taken from existing works, at analysing ASD screening training dataset. Result We tested Naive Bayes, Logistic Regression, KNN, J48, Random Forest, SVM, and Deep Neural Network algorithms to ASD screening dataset and compared the classifiers’ based on significant parameters; sensitivity, specificity, accuracy, receiver operating characteristic, area under the curve, and runtime, in predicting ASD occurrences. We also found that most of previous studies focused on classifying health-related dataset while ignoring the missing values which may contribute to significant impacts to the classification result which in turn may impact the life of the patients. Thus, we addressed the missing values by implementing imputation method where they are replaced with the mean of the available records found in the dataset. Conclusion We found that J48 produced promising results as compared to other classifiers when tested in both circumstances, with and without missing values. Our findings also suggested that SVM does not necessarily perform well for small and simple datasets. The outcome is hoped to assist health practitioners in making accurate diagnosis of ASD occurrences in patients.