Comparative Study of Technology With Student’s Perceptions in Indian and Hungarian Universities for Real-Time: Preliminary Results

Verma, Chaman; Illés, Zoltán; Stoffová, Veronika; Bakonyi, Viktoria

doi:10.1109/access.2021.3056592

Cited by 6 publications

(1 citation statement)

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“…The Student’s t -test was used to analyze the association of the continuous variable attributes, where the features are reserved if they show significant correlation (i.e., p -value < 0.05); otherwise, they are omitted [ 14 ]. The Mann–Whitney U-test is used to compare two population means without the assumption of being drawn from standard distribution [ 15 ].…”

Section: Methodsmentioning

confidence: 99%

Early-Stage Detection of Ovarian Cancer Based on Clinical Data Using Machine Learning Approaches

Ahamad

Aktar

Uddin

et al. 2022

JPM

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One of the common types of cancer for women is ovarian cancer. Still, at present, there are no drug therapies that can properly cure this deadly disease. However, early-stage detection could boost the life expectancy of the patients. The main aim of this work is to apply machine learning models along with statistical methods to the clinical data obtained from 349 patient individuals to conduct predictive analytics for early diagnosis. In statistical analysis, Student’s t-test as well as log fold changes of two groups are used to find the significant blood biomarkers. Furthermore, a set of machine learning models including Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Extreme Gradient Boosting Machine (XGBoost), Logistic Regression (LR), Gradient Boosting Machine (GBM) and Light Gradient Boosting Machine (LGBM) are used to build classification models to stratify benign-vs.-malignant ovarian cancer patients. Both of the analysis techniques recognized that the serumsamples carbohydrate antigen 125, carbohydrate antigen 19-9, carcinoembryonic antigen and human epididymis protein 4 are the top-most significant biomarkers as well as neutrophil ratio, thrombocytocrit, hematocrit blood samples, alanine aminotransferase, calcium, indirect bilirubin, uric acid, natriumas as general chemistry tests. Moreover, the results from predictive analysis suggest that the machine learning models can classify malignant patients from benign patients with accuracy as good as 91%. Since generally, early-stage detection is not available, machine learning detection could play a significant role in cancer diagnosis.

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Section: Methodsmentioning

confidence: 99%