Dementia Detection from Speech Using Machine Learning and Deep Learning Architectures

Kumar, M. Rupesh; Vekkot, Susmitha; Lalitha, S.; Gupta, Deepa; Govindraj, Varasiddhi Jayasuryaa; Shaukat, Kamran; Alotaibi, Yousef Ajami; Zakariah, Mohammed

doi:10.3390/s22239311

Cited by 41 publications

(10 citation statements)

References 49 publications

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“…Table 5 shows the result of the random undersampling technique with multiple feature selection techniques. The GB model performed poorly, with an accuracy of 68.1% in the case of ReliefF and 68.6% with the OneR feature selections technique [ 63 , 64 , 65 ]. The XGBOOST model performed very well for correlation and information gain feature selection techniques with an accuracy of 82.8%.…”

Section: Resultsmentioning

confidence: 99%

A Proposed Framework for Early Prediction of Schistosomiasis

et al. 2022

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Schistosomiasis is a neglected tropical disease that continues to be a leading cause of illness and mortality around the globe. The causing parasites are affixed to the skin through defiled water and enter the human body. Failure to diagnose Schistosomiasis can result in various medical complications, such as ascites, portal hypertension, esophageal varices, splenomegaly, and growth retardation. Early prediction and identification of risk factors may aid in treating disease before it becomes incurable. We aimed to create a framework by incorporating the most significant features to predict Schistosomiasis using machine learning techniques. A dataset of advanced Schistosomiasis has been employed containing recovery and death cases. A total data of 4316 individuals containing recovery and death cases were included in this research. The dataset contains demographics, socioeconomic, and clinical factors with lab reports. Data preprocessing techniques (missing values imputation, outlier removal, data normalisation, and data transformation) have also been employed for better results. Feature selection techniques, including correlation-based feature selection, Information gain, gain ratio, ReliefF, and OneR, have been utilised to minimise a large number of features. Data resampling algorithms, including Random undersampling, Random oversampling, Cluster Centroid, Near miss, and SMOTE, are applied to address the data imbalance problem. We applied four machine learning algorithms to construct the model: Gradient Boosting, Light Gradient Boosting, Extreme Gradient Boosting and CatBoost. The performance of the proposed framework has been evaluated based on Accuracy, Precision, Recall and F1-Score. The results of our proposed framework stated that the CatBoost model showed the best performance with the highest accuracy of (87.1%) compared with Gradient Boosting (86%), Light Gradient Boosting (86.7%) and Extreme Gradient Boosting (86.9%). Our proposed framework will assist doctors and healthcare professionals in the early diagnosis of Schistosomiasis.

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Section: Resultsmentioning

confidence: 99%

A Proposed Framework for Early Prediction of Schistosomiasis

et al. 2022

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“…The segment-level features are then produced using the DCNN model that has already been trained on the ImageNet dataset. The unweighted average recall (UAR) for experiments using the EMO-DB and IEMOCAP databases was 87.86 and 68.50 percent, respectively [ 22 , 23 , 24 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Human–Computer Interaction with a Real-Time Speech Emotion Recognition with Ensembling Techniques 1D Convolution Neural Network and Attention

Alsabhan

2023

Sensors

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Emotions have a crucial function in the mental existence of humans. They are vital for identifying a person’s behaviour and mental condition. Speech Emotion Recognition (SER) is extracting a speaker’s emotional state from their speech signal. SER is a growing discipline in human–computer interaction, and it has recently attracted more significant interest. This is because there are not so many universal emotions; therefore, any intelligent system with enough computational capacity can educate itself to recognise them. However, the issue is that human speech is immensely diverse, making it difficult to create a single, standardised recipe for detecting hidden emotions. This work attempted to solve this research difficulty by combining a multilingual emotional dataset with building a more generalised and effective model for recognising human emotions. A two-step process was used to develop the model. The first stage involved the extraction of features, and the second stage involved the classification of the features that were extracted. ZCR, RMSE, and the renowned MFC coefficients were retrieved as features. Two proposed models, 1D CNN combined with LSTM and attention and a proprietary 2D CNN architecture, were used for classification. The outcomes demonstrated that the suggested 1D CNN with LSTM and attention performed better than the 2D CNN. For the EMO-DB, SAVEE, ANAD, and BAVED datasets, the model’s accuracy was 96.72%, 97.13%, 96.72%, and 88.39%, respectively. The model beat several earlier efforts on the same datasets, demonstrating the generality and efficacy of recognising multiple emotions from various languages.

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“…They also performed occlusion analysis to reveal the most significant brain areas for the dementia subtypes classification task. Additionally, researchers also used cognitive data [56], speech features [57,58], and fundus photographs data [59] in deep learning models for dementia diagnosis.…”

Section: Deep Learning With Multimodal Data In Dementiamentioning

confidence: 99%

Deep learning applications in vascular dementia using neuroimaging

Dong,

Hayashi

2023

Current Opinion in Psychiatry

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Purpose of review Vascular dementia (VaD) is the second common cause of dementia after Alzheimer's disease, and deep learning has emerged as a critical tool in dementia research. The aim of this article is to highlight the current deep learning applications in VaD-related imaging biomarkers and diagnosis. Recent findings The main deep learning technology applied in VaD using neuroimaging data is convolutional neural networks (CNNs). CNN models have been widely used for lesion detection and segmentation, such as white matter hyperintensities (WMH), cerebral microbleeds (CMBs), perivascular spaces (PVS), lacunes, cortical superficial siderosis, and brain atrophy. Applications in VaD subtypes classification also showed excellent results. CNN-based deep learning models have potential for further diagnosis and prognosis of VaD. Summary Deep learning neural networks with neuroimaging data in VaD research represent significant promise for advancing early diagnosis and treatment strategies. Ongoing research and collaboration between clinicians, data scientists, and neuroimaging experts are essential to address challenges and unlock the full potential of deep learning in VaD diagnosis and management.

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Dementia Detection from Speech Using Machine Learning and Deep Learning Architectures

Cited by 41 publications

References 49 publications

A Proposed Framework for Early Prediction of Schistosomiasis

A Proposed Framework for Early Prediction of Schistosomiasis

Human–Computer Interaction with a Real-Time Speech Emotion Recognition with Ensembling Techniques 1D Convolution Neural Network and Attention

Deep learning applications in vascular dementia using neuroimaging

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