Statistical Modeling in Healthcare

Bahadori, Mina; Soltani, Morteza; Soleimani, Masoumeh; Bahadori, Mahsa

doi:10.4018/979-8-3693-0876-9.ch025

Cited by 8 publications

(3 citation statements)

References 50 publications

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“…The experimental setup entailed a random selection of images for both training and testing phases across all experiments. Specifically, two-thirds of the images sourced from each database were allocated for training purposes, while the remaining one-third constituted the testing set [41]. This balanced partitioning strategy ensures a comprehensive evaluation of the models' performance on distinct datasets.…”

Section: Lab Resultsmentioning

confidence: 99%

Improving recognition accuracy for facial expressions using scattering wavelet

Davari,

Harooni,

Nasr

et al. 2024

EAI Endorsed Trans AI Robotics

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One of the most evident and meaningful feedback about people’s emotions is through facial expressions. Facial expression recognition is helpful in social networks, marketing, and intelligent education systems. The use of Deep Learning based methods in facial expression identification is widespread, but challenges such as computational complexity and low recognition rate plague these methods. Scatter Wavelet is a type of Deep Learning that extracts features from Gabor filters in a structure similar to convolutional neural networks. This paper presents a new facial expression recognition method based on wavelet scattering that identifies six states: anger, disgust, fear, happiness, sadness, and surprise. The proposed method is simulated using the JAFFE and CK+ databases. The recognition rate of the proposed method is 99.7%, which indicates the superiority of the proposed method in recognizing facial expressions.

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Section: Lab Resultsmentioning

confidence: 99%

Improving recognition accuracy for facial expressions using scattering wavelet

Davari,

Harooni,

Nasr

et al. 2024

EAI Endorsed Trans AI Robotics

View full text Add to dashboard Cite

show abstract

“…Deep learning models have achieved impressive results in various fields, from natural language processing to medical image analysis , 2022, Bahadori et al 2023. These models depend on fine-tuning weights to closely align predicted results with actual data , Gharagozlou et al 2022.…”

Section: Introductionmentioning

confidence: 99%

ELRL-MD: a deep learning approach for myocarditis diagnosis using cardiac magnetic resonance images with ensemble and reinforcement learning integration

Mirzaee Moghaddam Kasmaee,

Ataei,

Moravvej

et al. 2024

Physiol. Meas.

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Objective. Myocarditis poses a significant health risk, often precipitated by viral infections like Coronavirus disease (COVID-19), and can lead to fatal cardiac complications. As a less invasive alternative to the standard diagnostic practice of endomyocardial biopsy, which is highly invasive and thus limited to severe cases, Cardiac Magnetic Resonance (CMR) imaging offers a promising solution for detecting myocardial abnormalities. Approach. This study introduces a deep model called ELRL-MD that combines ensemble learning and Reinforcement Learning (RL) for effective myocarditis diagnosis from CMR images. The model begins with pre-training via the Artificial Bee Colony (ABC) algorithm to enhance the starting point for learning. An array of Convolutional Neural Networks (CNNs) then works in concert to extract and integrate features from CMR images for accurate diagnosis. Leveraging the Z-Alizadeh Sani myocarditis CMR dataset, the model employs RL to navigate the dataset's imbalance by conceptualizing diagnosis as a decision-making process. Main Results. ELRL-DM demonstrates remarkable efficacy, surpassing other deep learning, conventional machine learning, and transfer learning models, achieving an F-measure of 88.2\% and a geometric mean of 90.6\%. Extensive experimentation helped pinpoint the optimal reward function settings and the perfect count of CNNs. Significance. The study addresses the primary technical challenge of inherent data imbalance in CMR imaging datasets and the risk of models converging on local optima due to suboptimal initial weight settings. Further analysis, leaving out ABC and RL components, confirmed their contributions to the model's overall performance, underscoring the effectiveness of addressing these critical technical challenges.

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“…Medical classification faces significant challenges due to data imbalance, which can significantly lower performance because there are far more negative instances [15,16]. Oversampling, under-sampling, or a compound of both are used in the data-level strategy to mitigate the negative effects of imbalanced classification [17,18].…”

Section: Introductionmentioning

confidence: 99%

A Focal Loss-based Multi-layer Perceptron for Diagnosis of Cardiovascular Risk in Athletes

Yang

2023

IJACSA

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Cardiovascular diseases (CVDs) are a prevalent cause of heart failure around the world. This research was required in order to investigate potential approaches to treating the disease. The article presents a focal loss (FL)-based multilayer perceptron called MLP-FL-CRD to diagnose cardiovascular risk in athletes. In 2012, 26,002 athletes were measured for their height, weight, age, sex, blood pressure, and pulse rate in a medical exam that had electrocardiography at rest. Outcomes were negative for the largest majority, leading to class imbalance. Training on imbalanced data hurts classifier performance. To address this, the study proposes a training approach based on focal loss, which effectively emphasizes minority class examples. Focal loss softens the influence of simplistic samples, enabling the model to concentrate on more intricate examples. It is useful in circumstances when there is a substantial class imbalance. Additionally, the paper highlights a challenge in the training phase, often characterized by the use of gradient-based learning methods like backpropagation. These methods exhibit several disadvantages, including sensitivity to initialization. The paper recommends the implementation of a mutual learning-based artificial bee colony (ML-ABC). This approach adjusts the primary weight by substituting the food resource candidate, which is selected due to superior fitness, with one based on a mutual learning factor between two individuals. The sample obtains great outcomes, outperforming other machine learning samples. Optimal values for important parameters are identified for the model based on experiments on the study dataset. Ablation studies that exclude FL and ML-ABC of the sample confirm the additive effect of, which is not negative and dependent, these factors on the sample's efficiency.

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Statistical Modeling in Healthcare

Cited by 8 publications

References 50 publications

Improving recognition accuracy for facial expressions using scattering wavelet

Improving recognition accuracy for facial expressions using scattering wavelet

ELRL-MD: a deep learning approach for myocarditis diagnosis using cardiac magnetic resonance images with ensemble and reinforcement learning integration

A Focal Loss-based Multi-layer Perceptron for Diagnosis of Cardiovascular Risk in Athletes

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