Enhanced abnormal data detection hybrid strategy based on heuristic and stochastic approaches for efficient patients rehabilitation

Khan, Murad Ali; Iqbal, Naeem; Jamil, Harun; Qayyum, Faiza; Jang, Jong-Hyun; Khan, Salabat; Kim, Jae-Chul; Kim, Do-Hyeun

doi:10.1016/j.future.2023.11.036

Cited by 5 publications

(2 citation statements)

References 62 publications

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“…Lin et al [47] investigated genotypic and phenotype information integration with multi-omics data. Khan et al [48] applied automated ML on multi-source data in response to the advent of big data of pa-tients rehabilitation. In addition, Ven et al [49] used multidimensional models to predict obesity by combining genetic, clinical, and environmental factors.…”

Section: Related Workmentioning

confidence: 99%

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Unraveling the Potential of Attentive Bi-LSTM for Accurate Obesity Prognosis: Advancing Public Health towards Sustainable Cities

Ayub,

Khan,

Shehryar Ali Naqvi

et al. 2024

Bioengineering

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The global prevalence of obesity presents a pressing challenge to public health and healthcare systems, necessitating accurate prediction and understanding for effective prevention and management strategies. This article addresses the need for improved obesity prediction models by conducting a comprehensive analysis of existing machine learning (ML) and deep learning (DL) approaches. This study introduces a novel hybrid model, Attention-based Bi-LSTM (ABi-LSTM), which integrates attention mechanisms with bidirectional Long Short-Term Memory (Bi-LSTM) networks to enhance interpretability and performance in obesity prediction. Our study fills a crucial gap by bridging healthcare and urban planning domains, offering insights into data-driven approaches to promote healthier living within urban environments. The proposed ABi-LSTM model demonstrates exceptional performance, achieving a remarkable accuracy of 96.5% in predicting obesity levels. Comparative analysis showcases its superiority over conventional approaches, with superior precision, recall, and overall classification balance. This study highlights significant advancements in predictive accuracy and positions the ABi-LSTM model as a pioneering solution for accurate obesity prognosis. The implications extend beyond healthcare, offering a precise tool to address the global obesity epidemic and foster sustainable development in smart cities.

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Section: Related Workmentioning

confidence: 99%

“…Khan et al. [ 48 ] applied automated ML on multi-source data in response to the advent of big data of patients rehabilitation. In addition, Ven et al.…”

Section: Related Workmentioning

confidence: 99%