Comparative Study of Classification Algorithms: Holdouts as Accuracy Estimation

Sondakh, Debby Erce

doi:10.31154/cogito.v1i1.2.13-23

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…In the random forest analysis, the number of available variables for splitting at each tree node was calculated as the square root of the number of predictor variables (rounded down). The repeated 3-fold stratified cross-validation approach was used to validate the models [ 50 , 51 ]. Due to the unbalanced nature of the dataset, the SMOTE technique was used to attenuate the bias towards the classification in the majority class in each training fold [ 52 ].…”

Section: Methodsmentioning

confidence: 99%

A Complex Radiomic Signature in Luminal Breast Cancer from a Weighted Statistical Framework: A Pilot Study

Castaldo¹,

Garbino²,

Cavaliere³

et al. 2022

Diagnostics

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Radiomics is rapidly advancing in precision diagnostics and cancer treatment. However, there are several challenges that need to be addressed before translation to clinical use. This study presents an ad-hoc weighted statistical framework to explore radiomic biomarkers for a better characterization of the radiogenomic phenotypes in breast cancer. Thirty-six female patients with breast cancer were enrolled in this study. Radiomic features were extracted from MRI and PET imaging techniques for malignant and healthy lesions in each patient. To reduce within-subject bias, the ratio of radiomic features extracted from both lesions was calculated for each patient. Radiomic features were further normalized, comparing the z-score, quantile, and whitening normalization methods to reduce between-subjects bias. After feature reduction by Spearman’s correlation, a methodological approach based on a principal component analysis (PCA) was applied. The results were compared and validated on twenty-seven patients to investigate the tumor grade, Ki-67 index, and molecular cancer subtypes using classification methods (LogitBoost, random forest, and linear discriminant analysis). The classification techniques achieved high area-under-the-curve values with one PC that was calculated by normalizing the radiomic features via the quantile method. This pilot study helped us to establish a robust framework of analysis to generate a combined radiomic signature, which may lead to more precise breast cancer prognosis.

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Section: Methodsmentioning

confidence: 99%

A Complex Radiomic Signature in Luminal Breast Cancer from a Weighted Statistical Framework: A Pilot Study

Castaldo¹,

Garbino²,

Cavaliere³

et al. 2022

Diagnostics

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show abstract

“…Ini sederhana dan mudah dipahami, dan pengguna tidak perlu melakukan banyak hal untuk menyiapkan data. Aturan dapat dibuat dengan cepat, dan kompleksitas masalah juga berkurang [13], [14]. DT ini merupakan algoritma non-parametrik sehingga dapat digunakan pada dataset yang besar dan kompleks tanpa memaksakan struktur parametrik yang sulit.…”

Section: A Latar Belakangunclassified

Emotion Mining User Review of the BRImo Mobile Banking Application Using the Decision Tree Algorithm

Sondakh,

Maringka,

Ayorbaba

et al. 2023

SISFOKOM

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As consumer transaction preferences shifted from analog to digital, banks were compelled to develop digital transactions in the form of mobile banking. Users of mobile banking provide feedback regarding the application's usability. The opinions of users can be emotive. Emotions influence what a person emits or applies. Emotions are the behavioral response of a person when he is happy or unhappy. Thus, the manifestation of a person's emotions, whether in the form of facial expressions, verbal communication, written text, or judgment, can be used as a source of information to aid in decision making. The objective of this study is to apply emotion mining to the analysis of user evaluations of the BRImo application, one of the three most popular platforms in Indonesia as of August 2022, with a total of 800,000 reviews on the Play Store. Emotion Mining can be used to analyze the four categories of emotions expressed by users in the comments section: happy, angry, sad, and afraid. According to BRImo user evaluations, the decision tree algorithm is used to categorize happy, sad, afraid, and angry feelings. Using a decision tree to manage large data category sets is effective. The obtained dataset included 2959 happy classes, 2196 sad classes, 387 angry classes, and 81 scared classes. According to the findings of the analysis, a significant number of users of the BRImo application express positive sentiments in their evaluations, which are indicative of happy emotions. The Decision Tree algorithm yields results with a performance specification of 84.5%, sensitivity of 85.5%, and precision of 84.4%.

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Comparative Study of Classification Algorithms: Holdouts as Accuracy Estimation

Cited by 2 publications

References 5 publications

A Complex Radiomic Signature in Luminal Breast Cancer from a Weighted Statistical Framework: A Pilot Study

A Complex Radiomic Signature in Luminal Breast Cancer from a Weighted Statistical Framework: A Pilot Study

Emotion Mining User Review of the BRImo Mobile Banking Application Using the Decision Tree Algorithm

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