Physical activity prediction using fitness data: Challenges and issues

Zakariya, Nur Zarna Elya Binti; Rosli, Marshima Mohd

doi:10.11591/eei.v10i1.2474

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…The gradient boosting framework is used by the decision tree-based ML method known as XGBoost [56], [57]. XGBoost was created by Tianqi Chen and was first kept up by the distributed (deep) machine learning community (DMLC).…”

Section: Xgboost With Vital Feature Combination Algorithmmentioning

confidence: 99%

Virtual analysis of machine learning models for diseases prediction in muskmelon

Kannan,

Amutha,

Dasarathan

et al. 2024

IJEECS

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Muskmelon, a crop prized for its economic potential, has a relatively brief growth cycle. Disease susceptibility during this period can have a profound impact on yields, posing challenges for farmers. Environmental conditions are pivotal in disease occurrence. Unfavorable conditions reduce the likelihood of pathogens infecting vulnerable host plants as temperature and humidity influence pathogen behavior, including toxin synthesis, virulence protein production, and reproduction. Pathogens can lie dormant in the soil until suitable conditions activate them. When the right environment and host plants align, these dormant pathogens can cause outbreaks. Disease prediction becomes possible by analyzing environmental variables. Real-time data collected via strategically placed sensors focused on viral, fungal, and bacterial infections. Results indicated that the extreme gradient boosting (XGBoost) algorithm, with a maximum tree depth of 4 and 30 trees per iteration, achieved remarkable performance, yielding an accuracy of 97%. For comparison, the XGBoost model outperformed an 8-layer Backpropagation network with 7 nodes per layer, which achieved 95% accuracy. These findings underscore XGBoost's efficacy in forecasting and mitigating muskmelon plant diseases, offering the potential for improved crop yields and agricultural sustainability.

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Section: Xgboost With Vital Feature Combination Algorithmmentioning

confidence: 99%

Virtual analysis of machine learning models for diseases prediction in muskmelon

Kannan,

Amutha,

Dasarathan

et al. 2024

IJEECS

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show abstract

“…He categorized and structured the published research evidence in the field of machine learning techniques for predicting physical activity using fitness data based on personal background and fitness data to predict appropriate physical activity. This research provided new insights into software development in healthcare technology to support the personalization of individuals in managing their own health (1). Wang H conducted inquiries, modifications, additions, deletions, etc.…”

Section: Related Workmentioning

confidence: 99%

Physical fitness data monitoring of college students based on the internet of things and blockchain

Sang

Wang

2022

Front. Public Health

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Contemporary college students are the pillars of the country and bear the responsibility of building a great country. College students should not only have smart brains, but also have strong bodies. The state has always attached great importance to the physical condition of college students and has promulgated a series of relevant policies and regulations to ensure the effective development of college students' physical health work. This paper aims to monitor and research college students' physical fitness data based on the Internet of Things and blockchain technology. This paper first introduces the data collection based on the Internet of Things, the Internet of Things data collection system has good versatility, ease of use, and quite rich functions, which can realize the collection and reliable transmission of different environmental data. Then focuses on the data collection and confidentiality technology based on blockchain. Each user in the blockchain system has a pair of public and private keys, and elliptic curve algorithms are usually used to generate public key cryptography. Finally, based on the Internet of Things and blockchain technology, the physical fitness data of college students is analyzed and researched. The experimental results of this paper show that, according to the data collection technology of the Internet of Things and blockchain, the analysis of variance is carried out on the data of male pull-ups and female sit-ups of 2019 students. The analysis of variance F of boys' pull-ups is 76.222, and the significance is about 0, that is, P < 0.01. The difference is very obvious, which proves that there is a significant difference in boys' pull-ups in the past 3 years. The analysis of variance F for girls' sit-ups is 89.187, and the significance is about 0. Similarly, it shows that there are significant differences in girls' sit-ups in the past 3 years. Therefore, the existing teaching mode is stabilized and physical exercise is enhanced. Meanwhile, to enhance the physical fitness of students, it is necessary to strengthen the strength of physical education teachers and increase the introduction of sports talents and business training.

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“…Ref. [ 17 ] applied the Fogg behavior model and the transtheoretical behavior model to predict physical activity. In [ 18 ], the authors examined the association between physical activity and the expiratory to inspiratory (E/I) ratio of mean lung density (MLD) and showed that the E/I ratio of MLD could be a useful imaging biomarker for the early detection of physical inactivity in COPD patients.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Approach to Investigating the Importance of Test Items for Predicting Physical Activity in Chronic Obstructive Pulmonary Disease

Nakahara

Mabu

Hirano

et al. 2023

JCM

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Contracting COPD reduces a patient’s physical activity and restricts everyday activities (physical activity disorder). However, the fundamental cause of physical activity disorder has not been found. In addition, costly and specialized equipment is required to accurately examine the disorder; hence, it is not regularly assessed in normal clinical practice. In this study, we constructed a machine learning model to predict physical activity using test items collected during the normal care of COPD patients. In detail, we first applied three types of data preprocessing methods (zero-padding, multiple imputation by chained equations (MICE), and k-nearest neighbor (kNN)) to complement missing values in the dataset. Then, we constructed several types of neural networks to predict physical activity. Finally, permutation importance was calculated to identify the importance of the test items for prediction. Multifactorial analysis using machine learning, including blood, lung function, walking, and chest imaging tests, was the unique point of this research. From the experimental results, it was found that the missing value processing using MICE contributed to the best prediction accuracy (73.00%) compared to that using zero-padding (68.44%) or kNN (71.52%), and showed better accuracy than XGBoost (66.12%) with a significant difference (p < 0.05). For patients with severe physical activity reduction (total exercise < 1.5), a high sensitivity (89.36%) was obtained. The permutation importance showed that “sex, the number of cigarettes, age, and the whole body phase angle (nutritional status)” were the most important items for this prediction. Furthermore, we found that a smaller number of test items could be used in ordinary clinical practice for the screening of physical activity disorder.

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Physical activity prediction using fitness data: Challenges and issues

Cited by 7 publications

References 30 publications

Virtual analysis of machine learning models for diseases prediction in muskmelon

Virtual analysis of machine learning models for diseases prediction in muskmelon

Physical fitness data monitoring of college students based on the internet of things and blockchain

Neural Network Approach to Investigating the Importance of Test Items for Predicting Physical Activity in Chronic Obstructive Pulmonary Disease

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