Prospects and Pitfalls of Machine Learning in Nutritional Epidemiology

Russo, Stefania; Bonassi, Stefano

doi:10.3390/nu14091705

Cited by 8 publications

(4 citation statements)

References 97 publications

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“…Another approach is to use a methodology for feature selection using machine learning on a large dataset. [ 52 ] Although more data improve quality, there is a possibility that too much data distorts the model. In view of ensuring standards for AI/ML, it was issued in the USA an Executive Order naming “Maintaining American Leadership in Artificial Intelligence” to install principles and strategies to improve AI practice.…”

Section: Artificial Intelligence and Machine Learningmentioning

confidence: 99%

Role of Artificial Intelligence in Multinomial Decisions and Preventative Nutrition in Alzheimer's Disease

Soares Dias Portela,

Saxena,

Rosenn

et al. 2024

Molecular Nutrition Food Res

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Alzheimer's disease (AD) affects 50 million people worldwide, an increase of 35 million since 2015, and it is known for memory loss and cognitive decline. Considering the morbidity associated with AD, it is important to explore lifestyle elements influencing the chances of developing AD, with special emphasis on nutritional aspects. This review will first discuss how dietary factors have an impact in AD development and the possible role of Artificial Intelligence (AI) and Machine Learning (ML) in preventative care of AD patients through nutrition. The Mediterranean‐DASH diets provide individuals with many nutrient benefits which assists the prevention of neurodegeneration by having neuroprotective roles. Lack of micronutrients, protein‐energy, and polyunsaturated fatty acids increase the chance of cognitive decline, loss of memory, and synaptic dysfunction among others.ML software has the ability to design models of algorithms from data introduced to present practical solutions that are accessible and easy to use. It can give predictions for a precise medicine approach to evaluate individuals as a whole. There is no doubt the future of nutritional science lies on customizing diets for individuals to reduce dementia risk factors, maintain overall health and brain function.

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Section: Artificial Intelligence and Machine Learningmentioning

confidence: 99%

Role of Artificial Intelligence in Multinomial Decisions and Preventative Nutrition in Alzheimer's Disease

Soares Dias Portela,

Saxena,

Rosenn

et al. 2024

Molecular Nutrition Food Res

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“…Furthermore, K-means clustering is sensitive to noise that can be interpreted as some mislabeled samples. Besides, it may lead to possible sub-optimal clustering results because of the trapping in the local minimum easily [50].…”

Section: Mislabeled Sample Detection and Relabeling Via Pmap Clusteringmentioning

confidence: 99%

Mislabeled learning for psychiatric disorder detection

Liu

Han

2022

Preprint

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The misdiagnosis between schizophrenia (SCZ) and bipolar disorder (BPD) has been a challenge in psychiatry. It is also a long-time unsolved mislabeled learning problem in machine learning and AI fields. In this study, we propose a psychiatric map (pMAP) diagnosis, which is built upon a novel feature self-organizing map (fSOM) algorithm, to tackle it. The psychiatric map summarizes the latent essential characteristics of each observation on a two-dimensional fSOM plane. It solves the misdiagnosis problem by providing high-accuracy psychiatric detection via automatically mislabeled observation identification. Furthermore, pMAP provides powerful and informative visualization for each observation in unveiling hidden psychiatric subtype discovery. This study also presents new insight into the pathology of psychiatric disorders by constructing the devolution path of psychiatric states via relative entropy analysis that discloses latent internal transfer and devolution road maps between different subtypes of the control, BPD, and SCZ groups. To the best of our knowledge, it is the first study to solve mislabel learning for high-dimensional data in machine learning and will inspire more future work in this field.

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“…[14] Machine learning methods, unlike traditional regression models, have the ability to detect and analyze intricate relationships between nutrient intake and CVD. [14,15] Therefore, our Medicine objective was to develop a machine learning model using data from a substantial UK Biobank cohort to forecast the likelihood of CVD based on the consumption of various micronutrients, as well as to assess the significance of each nutrient in relation to this risk. This insight could hold clinical importance and enhance the strategies for preventing and treating CVD in the adult population.…”

Section: Introductionmentioning

confidence: 99%

Dietary intake of potassium, vitamin E, and vitamin C emerges as the most significant predictors of cardiovascular disease risk in adults

Wang,

Han,

Ling

et al. 2024

Medicine

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Prediction models were developed to assess the risk of cardiovascular disease (CVD) based on micronutrient intake, utilizing data from 90,167 UK Biobank participants. Four machine learning models were employed to predict CVD risk, with performance evaluation metrics including area under the receiver operating characteristic curve (AUC), accuracy, recall, specificity, and F1-score. The eXtreme Gradient Boosting (XGBoost) model was utilized to rank the importance of 11 micronutrients in cardiovascular health. Results indicated that vitamin E, calcium, vitamin C, and potassium intake were associated with a reduced risk of CVD. The XGBoost model demonstrated the highest performance with an AUC of 0.952, highlighting potassium, vitamin E, and vitamin C as key predictors of CVD risk. Subgroup analysis revealed a stronger correlation between calcium intake and CVD risk in older adults and those with higher BMI, while vitamin B6 intake showed a link to CVD risk in women. Overall, the XGBoost model emphasized the significance of potassium, vitamin E, and vitamin C intake as primary predictors of CVD risk in adults, with age, sex, and BMI potentially influencing the importance of micronutrient intake in predicting CVD risk.

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Prospects and Pitfalls of Machine Learning in Nutritional Epidemiology

Cited by 8 publications

References 97 publications

Role of Artificial Intelligence in Multinomial Decisions and Preventative Nutrition in Alzheimer's Disease

Role of Artificial Intelligence in Multinomial Decisions and Preventative Nutrition in Alzheimer's Disease

Mislabeled learning for psychiatric disorder detection

Dietary intake of potassium, vitamin E, and vitamin C emerges as the most significant predictors of cardiovascular disease risk in adults

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