The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification

D Chicco, G Jurman - BioData Mining, 2023 - Springer
Binary classification is a common task for which machine learning and computational
statistics are used, and the area under the receiver operating characteristic curve (ROC …

Ten quick tips for avoiding pitfalls in multi-omics data integration analyses

D Chicco, F Cumbo, C Angione - PLoS computational biology, 2023 - journals.plos.org
Data are the most important elements of bioinformatics: Computational analysis of
bioinformatics data, in fact, can help researchers infer new knowledge about biology …

Ten quick tips for electrocardiogram (ECG) signal processing

D Chicco, AI Karaiskou, M De Vos - PeerJ Computer Science, 2024 - peerj.com
The electrocardiogram (ECG) is a powerful tool to measure the electrical activity of the heart,
and the analysis of its data can be useful to assess the patient's health. In particular, the …

Machine learning approach using 18F-FDG-PET-radiomic features and the visibility of right ventricle 18F-FDG uptake for predicting clinical events in patients with …

M Nakajo, D Hirahara, M **guji, S Ojima… - Japanese Journal of …, 2024 - Springer
Objectives To investigate the usefulness of machine learning (ML) models using
pretreatment 18F-FDG-PET-based radiomic features for predicting adverse clinical events …

Robust cardiac segmentation corrected with heuristics

A Cervantes-Guzmán, K McPherson, J Olveres… - PLoS …, 2023 - journals.plos.org
Cardiovascular diseases related to the right side of the heart, such as Pulmonary
Hypertension, are some of the leading causes of death among the Mexican (and worldwide) …

Application of Machine Learning Analyses Using Clinical and [18F]-FDG-PET/CT Radiomic Characteristics to Predict Recurrence in Patients with Breast Cancer

K Kawaji, M Nakajo, Y Shinden, M **guji… - Molecular Imaging and …, 2023 - Springer
Purpose To develop and identify machine learning (ML) models using pretreatment clinical
and 2-deoxy-2-[18F] fluoro-d-glucose positron emission tomography ([18F]-FDG-PET) …

Machine learning-based prognostic modeling in gallbladder cancer using clinical data and pre-treatment [18F]-FDG-PET-radiomic features

M Nakajo, D Hirahara, M **guji, T Idichi… - Japanese Journal of …, 2024 - Springer
Objectives This study evaluates the effectiveness of machine learning (ML) models that
incorporate clinical and 2-deoxy-2-[18 F] fluoro-D-glucose ([18 F]-FDG)-positron emission …

Applying deep learning-based ensemble model to [18F]-FDG-PET-radiomic features for differentiating benign from malignant parotid gland diseases

M Nakajo, D Hirahara, M **guji, M Hirahara… - Japanese Journal of …, 2024 - Springer
Objectives To develop and identify machine learning (ML) models using pretreatment 2-
deoxy-2-[18F] fluoro-D-glucose ([18F]-FDG)-positron emission tomography (PET)-based …

[PDF][PDF] Machine Learning Analysis of Predictors for Inhaled Nitric Oxide Therapy Administration Time Post Congenital Heart Disease Surgery: A Single-Center …

S Niiyama, T Nakashima, K Ueno, D Hirahara… - Cureus, 2024 - cureus.com
Background Congenital heart disease (CHD) is a structural deformity of the heart present at
birth. Pulmonary hypertension (PH) may arise from increased blood flow to the lungs …

Machine learning based prediction model for acute coronary syndrome using biomarker

S Hajare, R Rewatkar, KTV Reddy - AIP Conference Proceedings, 2024 - pubs.aip.org
Cardio-vascular diseases, particularly acute coronary disease, remain a leading cause of
mortality worldwide. Timely and accurate prediction of heart disease risk is essential for …