Explainable Multi-class Classification of the CAMH COVID-19 Mental
Health Data
- URL: http://arxiv.org/abs/2105.13430v1
- Date: Thu, 27 May 2021 20:08:58 GMT
- Title: Explainable Multi-class Classification of the CAMH COVID-19 Mental
Health Data
- Authors: YuanZheng Hu and Marina Sokolova
- Abstract summary: We present explainable multi-class classification of the Covid-19 mental health data.
In Machine Learning study, we aim to find the potential factors to influence a personal mental health during the Covid-19 pandemic.
- Score: 0.9137554315375922
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Application of Machine Learning algorithms to the medical domain is an
emerging trend that helps to advance medical knowledge. At the same time, there
is a significant a lack of explainable studies that promote informed,
transparent, and interpretable use of Machine Learning algorithms. In this
paper, we present explainable multi-class classification of the Covid-19 mental
health data. In Machine Learning study, we aim to find the potential factors to
influence a personal mental health during the Covid-19 pandemic. We found that
Random Forest (RF) and Gradient Boosting (GB) have scored the highest accuracy
of 68.08% and 68.19% respectively, with LIME prediction accuracy 65.5% for RF
and 61.8% for GB. We then compare a Post-hoc system (Local Interpretable
Model-Agnostic Explanations, or LIME) and an Ante-hoc system (Gini Importance)
in their ability to explain the obtained Machine Learning results. To the best
of these authors knowledge, our study is the first explainable Machine Learning
study of the mental health data collected during Covid-19 pandemics.
Related papers
- Predictive Modeling for Breast Cancer Classification in the Context of Bangladeshi Patients: A Supervised Machine Learning Approach with Explainable AI [0.0]
We evaluate and compare the classification accuracy, precision, recall, and F-1 scores of five different machine learning methods.
XGBoost achieved the best model accuracy, which is 97%.
arXiv Detail & Related papers (2024-04-06T17:23:21Z) - COVIDHealth: A Benchmark Twitter Dataset and Machine Learning based Web
Application for Classifying COVID-19 Discussions [1.4018975578160688]
We label COVID-19-related Twitter data, provide benchmark classification results, and develop a web application.
We extracted features using various feature extraction methods and applied them to seven different traditional machine learning algorithms.
The Linear SVC algorithm exhibited the highest F1 score at 86.13%, surpassing other traditional machine learning approaches.
arXiv Detail & Related papers (2024-02-15T11:45:34Z) - UniBrain: Universal Brain MRI Diagnosis with Hierarchical
Knowledge-enhanced Pre-training [66.16134293168535]
We propose a hierarchical knowledge-enhanced pre-training framework for the universal brain MRI diagnosis, termed as UniBrain.
Specifically, UniBrain leverages a large-scale dataset of 24,770 imaging-report pairs from routine diagnostics.
arXiv Detail & Related papers (2023-09-13T09:22:49Z) - A Survey on the Role of Artificial Intelligence in the Prediction and
Diagnosis of Schizophrenia [0.0]
This survey aims to review papers that have focused on the use of deep learning to detect and predict schizophrenia.
With our chosen search strategy, we assessed ten publications from 2019 to 2022.
All studies achieved successful predictions of more than 80%.
In the field of artificial intelligence (AI) and machine learning (ML) for schizophrenia, significant advances have been made.
arXiv Detail & Related papers (2023-05-19T08:21:02Z) - NeuroExplainer: Fine-Grained Attention Decoding to Uncover Cortical
Development Patterns of Preterm Infants [73.85768093666582]
We propose an explainable geometric deep network dubbed NeuroExplainer.
NeuroExplainer is used to uncover altered infant cortical development patterns associated with preterm birth.
arXiv Detail & Related papers (2023-01-01T12:48:12Z) - When Accuracy Meets Privacy: Two-Stage Federated Transfer Learning
Framework in Classification of Medical Images on Limited Data: A COVID-19
Case Study [77.34726150561087]
COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources.
CNN has been widely utilized and verified in analyzing medical images.
arXiv Detail & Related papers (2022-03-24T02:09:41Z) - SSD-KD: A Self-supervised Diverse Knowledge Distillation Method for
Lightweight Skin Lesion Classification Using Dermoscopic Images [62.60956024215873]
Skin cancer is one of the most common types of malignancy, affecting a large population and causing a heavy economic burden worldwide.
Most studies in skin cancer detection keep pursuing high prediction accuracies without considering the limitation of computing resources on portable devices.
This study specifically proposes a novel method, termed SSD-KD, that unifies diverse knowledge into a generic KD framework for skin diseases classification.
arXiv Detail & Related papers (2022-03-22T06:54:29Z) - Medical-VLBERT: Medical Visual Language BERT for COVID-19 CT Report
Generation With Alternate Learning [70.71564065885542]
We propose to use the medical visual language BERT (Medical-VLBERT) model to identify the abnormality on the COVID-19 scans.
This model adopts an alternate learning strategy with two procedures that are knowledge pretraining and transferring.
For automatic medical report generation on the COVID-19 cases, we constructed a dataset of 368 medical findings in Chinese and 1104 chest CT scans.
arXiv Detail & Related papers (2021-08-11T07:12:57Z) - Explainable Multi-class Classification of Medical Data [0.9137554315375922]
We present explainable multi-class classification of a large medical data set.
Six algorithms are used in this study: Support Vector Machine (SVM), Na"ive Bayes, Gradient Boosting, Decision Trees, Random Forest, and Logistic Regression.
Our results show that using 23 medication features in learning experiments improves Recall of five out of the six applied learning algorithms.
arXiv Detail & Related papers (2020-12-26T18:56:07Z) - A Transfer Learning Based Active Learning Framework for Brain Tumor
Classification [10.437969366798411]
We propose a novel transfer learning based active learning framework to reduce the annotation cost.
We employ a 2D slice-based approach to train and finetune our model on the Magnetic Resonance Imaging (MRI) training dataset of 203 patients.
With our proposed method, the model achieved Area Under Receiver Operating Characteristic (ROC) Curve (AUC) of 82.89% on a separate test dataset of 66 patients.
arXiv Detail & Related papers (2020-11-16T21:11:40Z) - Patch-based Brain Age Estimation from MR Images [64.66978138243083]
Brain age estimation from Magnetic Resonance Images (MRI) derives the difference between a subject's biological brain age and their chronological age.
Early detection of neurodegeneration manifesting as a higher brain age can potentially facilitate better medical care and planning for affected individuals.
We develop a new deep learning approach that uses 3D patches of the brain as well as convolutional neural networks (CNNs) to develop a localised brain age estimator.
arXiv Detail & Related papers (2020-08-29T11:50:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.