Modelling of Engineering Systems With Small Data

Mohammadzaheri, Morteza; Ghodsi, Mojtaba; Ziaiefar, Hamidreza; Bahadur, Issam; Zarog, Musaab; Emadi, Mohammadreza; Soltani, Payam; Amouzadeh, Amirhosein

doi:10.4018/978-1-6684-7684-0.ch006

Cited by 3 publications

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Multimodal dementia identification using lifestyle and brain lesions, a machine learning approach

Akbarifar,

Maghsoudpour,

Mohammadian

et al. 2024

AIP Advances

Self Cite

View full text Add to dashboard Cite

Dementia diagnosis often relies on expensive and invasive neuroimaging techniques that limit access to early screening. This study proposes an innovative approach for facilitating early dementia screening by estimating diffusion tensor imaging (DTI) measures using accessible lifestyle and brain imaging factors. Conventional DTI analysis, though effective, is often hindered by high costs and limited accessibility. To address this challenge, fuzzy subtractive clustering identified 14 influential variables from the Lifestyle for Brain Health and Brain Atrophy and Lesion Index frameworks, encompassing demographics, medical conditions, lifestyle factors, and structural brain markers. A multilayer perceptron (MLP) neural network was developed using these selected variables to predict fractional anisotropy (FA), a DTI metric reflecting white matter integrity and cognitive function. The MLP model achieved promising results, with a mean squared error of 0.000 878 on the test set for FA prediction, demonstrating its potential for accurate DTI estimation without costly neuroimaging techniques. The FA values in the dataset ranged from 0 to 1, with higher values indicating greater white matter integrity. Thus, a mean squared error of 0.000 878 suggests that the model’s predictions were highly accurate compared to the observed FA values. This multifactorial approach aligns with the current understanding of dementia’s complex etiology influenced by various biological, environmental, and lifestyle factors. By integrating readily available data into a predictive model, this method enables widespread, cost-effective screening for early dementia risk assessment. The proposed accessible screening tool could facilitate timely interventions, preventive strategies, and efficient resource allocation in public health programs, ultimately improving patient outcomes and caregiver burden.

show abstract

Multimodal dementia identification using lifestyle and brain lesions, a machine learning approach

Akbarifar,

Maghsoudpour,

Mohammadian

et al. 2024

AIP Advances

Self Cite

View full text Add to dashboard Cite

show abstract

Predicting dementia progression with fully connected cascade neural networks

Akbarifar,

Maghsoudpour,

Mohammadian

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Accurate and timely diagnosis of dementia progression remains a major global challenge due to the complexities of brain pathology and the lack of definitive biomarkers. This study presents a pioneering fully connected cascade (FCC) neural network model that leverages cost-effective lifestyle and neuroimaging data to predict dementia progression with remarkable accuracy. The model uniquely integrates 42 lifestyle factors for brain health (LIBRA) and 7 brain atrophy and lesion indice (BALI) derived from baseline MRI data as inputs, to predict sensitive diffusion tensor imaging (DTI) biomarkers of white matter degeneration. Remarkably, the FCC network achieved a mean squared error of 0.0071693 in predicting DTI metrics, demonstrating exceptional predictive capability. This multidisciplinary data-driven approach capitalizes on the model's ability to detect subtle yet informative changes in brain structure and function through advanced neuroimaging. By amalgamating multidomain lifestyle and neuroimaging data, the proposed model enhances diagnostic value and sensitivity to dementia pathology. Its high accuracy, scalability with large datasets, clinical interpretability, and cost-effectiveness make it a powerful computational tool for early prediction, monitoring, and personalized treatment planning in dementia care. This groundbreaking research exemplifies the transformative potential of artificial intelligence in tackling the global dementia burden, paving the way for improved patient outcomes and reduced healthcare costs.

show abstract