Decision tree learning to predict overweight/obesity based on body mass index and gene polymporphisms

Systematic Review of Machine Learning applied to the Prediction of Obesity and Overweight

Obesity and overweight has increased in the last year and has become a pandemic disease, the result of sedentary lifestyles and unhealthy diets rich in sugars, refined starches, fats and calories. Machine learning (ML) has proven to be very useful in the scientific community, especially in the health sector. With the aim of providing useful tools to help nutritionists and dieticians, research focused on the development of ML and Deep Learning (DL) algorithms and models is searched in the literature. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol has been used, a very common technique applied to carry out revisions. In our proposal, 17 articles have been filtered in which ML and DL are applied in the prediction of diseases, in the delineation of treatment strategies, in the improvement of personalized nutrition and more. Despite expecting better results with the use of DL, according to the selected investigations, the traditional methods are still the most used and the yields in both cases fluctuate around positive values, conditioned by the databases (transformed in each case) to a greater extent than by the artificial intelligence paradigm used. Conclusions: An important compilation is provided for the literature in this area. ML models are time-consuming to clean data, but (like DL) they allow automatic modeling of large volumes of data which makes them superior to traditional statistics.

Machine Learning in Nutrition Research

Data currently generated in the field of nutrition are becoming increasingly complex and high-dimensional, bringing with them new methods of data analysis. The characteristics of machine learning (ML) make it suitable for such analysis and thus lend itself as an alternative tool to deal with data of this nature. ML has already been applied in important problem areas in nutrition, such as obesity, metabolic health, and malnutrition. Despite this, experts in nutrition are often without an understanding of ML, which limits its application and therefore potential to solve currently open questions. The current article aims to bridge this knowledge gap by supplying nutrition researchers with a resource to facilitate the use of ML in their research. ML is first explained and distinguished from existing solutions, with key examples of applications in the nutrition literature provided. Two case studies of domains in which ML is particularly applicable, precision nutrition and metabolomics, are then presented. Finally, a framework is outlined to guide interested researchers in integrating ML into their work. By acting as a resource to which researchers can refer, we hope to support the integration of ML in the field of nutrition to facilitate modern research.

Combined Association Between ADIPOQ, PPARG, and TNF Genes Variants and Obstructive Sleep Apnea in Chinese Han Population

PURPOSE

Obstructive sleep apnea (OSA) is a common chronic polygenic disease. Multiple genetic markers associated with OSA have been identified by genome-wide association studies. Here, we aimed to construct a polygenic risk score (PRS) and examine the association with the presence of OSA in a Chinese Han Population.

PATIENTS AND METHODS

This study included 1057 individuals who were genotyped for nine susceptibility loci from three genes (ADIPOQ, PPARG, and TNF), from which each individual's PRS was calculated by summing the number of risk alleles. The associations between PRS and OSA were determined by logistic regression analyses. Model discrimination was assessed by a receiver operating characteristic (ROC) curve using bootstrapping with 1000 resamples.

RESULTS

The subjects included 874 with OSA and 183 controls. A higher PRS was associated with an increased apnea-hypopnea index (AHI). The PRS was an important risk factor for the development of OSA (OR = 1.237 per SD, P = 0.030). Subjects with higher PRS had a 2.88-fold (95% CI: 1.393-5.955, P = 0.004) and 5.402-fold (95% CI: 2.311-12.624, P<0.001) greater risk for having OSA and moderate-to-severe OSA, respectively, compared with those with lower genetic risk. More importantly, compared with determination of risk based solely on clinical factors, addition of the PRS increased discriminatory accuracy for both OSA (AUC from 0.75 to 0.78, P = 0.02) and moderate-to-severe OSA (AUC from 0.80 to 0.83, P = 0.02).

CONCLUSION

Our study suggests that the PRS is independently associated with AHI and OSA. Combining PRS with conventional risk factors could improve the discrimination of OSA.

Cognitive Computing-Based CDSS in Medical Practice

Importance. The last decade has witnessed the advances of cognitive computing technologies that learn at scale and reason with purpose in medicine studies. From the diagnosis of diseases till the generation of treatment plans, cognitive computing encompasses both data-driven and knowledge-driven machine intelligence to assist health care roles in clinical decision-making. This review provides a comprehensive perspective from both research and industrial efforts on cognitive computing-based CDSS over the last decade.Highlights. (1) A holistic review of both research papers and industrial practice about cognitive computing-based CDSS is conducted to identify the necessity and the characteristics as well as the general framework of constructing the system. (2) Several of the typical applications of cognitive computing-based CDSS as well as the existing systems in real medical practice are introduced in detail under the general framework. (3) The limitations of the current cognitive computing-based CDSS is discussed that sheds light on the future work in this direction.Conclusion. Different from medical content providers, cognitive computing-based CDSS provides probabilistic clinical decision support by automatically learning and inferencing from medical big data. The characteristics of managing multimodal data and computerizing medical knowledge distinguish cognitive computing-based CDSS from other categories. Given the current status of primary health care like high diagnostic error rate and shortage of medical resources, it is time to introduce cognitive computing-based CDSS to the medical community which is supposed to be more open-minded and embrace the convenience and low cost but high efficiency brought by cognitive computing-based CDSS.

Precision nutrition: A systematic literature review

Precision Nutrition research aims to use personal information about individuals or groups of individuals to deliver nutritional advice that, theoretically, would be more suitable than generic advice. Machine learning, a subbranch of Artificial Intelligence, has promise to aid in the development of predictive models that are suitable for Precision Nutrition. As such, recent research has applied machine learning algorithms, tools, and techniques in precision nutrition for different purposes. However, a systematic overview of the state-of-the-art on the use of machine learning in Precision Nutrition is lacking. Therefore, we carried out a Systematic Literature Review (SLR) to provide an overview of where and how machine learning has been used in Precision Nutrition from various aspects, what such machine learning models use as input features, what the availability status of the data used in the literature is, and how the models are evaluated. Nine research questions were defined in this study. We retrieved 4930 papers from electronic databases and 60 primary studies were selected to respond to the research questions. All of the selected primary studies were also briefly discussed in this article. Our results show that fifteen problems spread across seven domains of nutrition and health are present. Four machine learning tasks are seen in the form of regression, classification, recommendation and clustering, with most of these utilizing a supervised approach. In total, 30 algorithms were used, with 19 appearing more than once. Models were through the use of four groups of approaches and 23 evaluation metrics. Personalized approaches are promising to reduce the burden of these current problems in nutrition research, and the current review shows Machine Learning can be incorporated into Precision Nutrition research with high performance. Precision Nutrition researchers should consider incorporating Machine Learning into their methods to facilitate the integration of many complex features, allowing for the development of high-performance Precision Nutrition approaches.

Association between diabetic retinopathy and interleukin-related gene polymorphisms: a machine learning aided meta-analysis

BACKGROUND

Diabetic retinopathy (DR) is a severe complication of diabetes and a common cause of visual loss in adults. We aimed to assess the correlation between IL gene-related SNPs and the incidence of DR and attempted to predict DR with combined mutation site detection.

METHODS

A systematic search of databases was performed up to August 2019. Five genetic models were used to analyze associations. Machine learning methods were implemented to improve SNP-related disease prediction.

RESULTS

Sixteen trials assessing a total of 7221 patients were included in our meta-analysis. IL6/rs1800795, rs1800796, and IL10/rs1800896 were analyzed. For the IL-6 gene, there was no significant association between rs1800795 and the incidence of DR (allelic model: OR, 1.091; 95% CI, 0.892-1.334; p = .396). There was no significant correlation between rs1800796 (allelic model: OR, 1.135; 95% CI, 0.678-1.901; p = .63), rs1800896 (allelic model: OR, 1.047; 95% CI, 0.788-1.392; p = .752) and the incidence of DR. Unfortunately, the machine learning results also showed that the combined detection of two SNPs could not accurately predict DR occurrence.

CONCLUSION

rs1800795 and rs1800796 in the IL-6 gene and rs1800896 in IL-10 gene are not related to the incidence of DR. Mutations in multiple SNPs for each DR patient still need to be specifically assessed to increase prediction accuracy.

A Machine Learning Approach to Short-Term Body Weight Prediction in a Dietary Intervention Program

Weight and obesity management is one of the emerging challenges in current health management. Nutrient-gene interactions in human obesity (NUGENOB) seek to find various solutions to challenges posed by obesity and over-weight. This research was based on utilising a dietary intervention method as a means of addressing the problem of managing obesity and overweight. The dietary intervention program was done for a period of ten weeks. Traditional statistical techniques have been utilised in analyzing the potential gains in weight and diet intervention programs. This work investigates the applicability of machine learning to improve on the prediction of body weight in a dietary intervention program. Models that were utilised include Dynamic model, Machine Learning models (Linear regression, Support vector machine (SVM), Random Forest (RF), Artificial Neural Networks (ANN)). The performance of these estimation models was compared based on evaluation metrics like RMSE, MAE and R2. The results indicate that the Machine learning models (ANN and RF) perform better than the other models in predicting body weight at the end of the dietary intervention program.