Nutritional biomarkers and machine learning for personalized nutrition applications and health optimization

2021 ◽  
pp. 1-9
Author(s):  
Dimitrios P. Panagoulias ◽  
Dionisios N. Sotiropoulos ◽  
George A. Tsihrintzis
Keyword(s):  
Machine Learning ◽  
Physiological State ◽  
Prediction Method ◽  
Disease Diagnosis ◽  
Classification Model ◽  
Cell Physiology ◽  
Cellular Processes ◽  
General Evaluation ◽  
Model Training ◽  
The One

The doctrine of the “one size fits all” approach in the field of disease diagnosis and patient management is being replaced by a more per patient approach known as “personalized medicine”. In this spirit, biomarkers are key variables in the research and development of new methods for prognostic and classification model training based on advances in the field of artificial intelligence [1, 2, 3]. Metabolomics refers to the systematic study of the unique chemical fingerprints that cellular processes leave behind. The metabolic profile of a person can provide a snapshot of cell physiology and, by extension, metabolomics provide a direct “functional reading of the physiological state” of an organism. Via employing machine learning methodologies, a general evaluation chart of nutritional biomarkers is formulated and an optimised prediction method for body to mass index is investigated with the aim to discover dietary patterns.

scholarly journals IIMLP: integrated information-entropy-based method for LncRNA prediction

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Junyi Li ◽  
Huinian Li ◽  
Xiao Ye ◽  
Li Zhang ◽  
Qingzhe Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dna Sequences ◽  
Information Entropy ◽  
Area Under The Curve ◽  
Prediction Method ◽  
Machine Learning Algorithms ◽  
Reading Frame ◽  
Non Coding Rna ◽  
The One ◽  
Long Non Coding Rna

Abstract Background The prediction of long non-coding RNA (lncRNA) has attracted great attention from researchers, as more and more evidence indicate that various complex human diseases are closely related to lncRNAs. In the era of bio-med big data, in addition to the prediction of lncRNAs by biological experimental methods, many computational methods based on machine learning have been proposed to make better use of the sequence resources of lncRNAs. Results We developed the lncRNA prediction method by integrating information-entropy-based features and machine learning algorithms. We calculate generalized topological entropy and generate 6 novel features for lncRNA sequences. By employing these 6 features and other features such as open reading frame, we apply supporting vector machine, XGBoost and random forest algorithms to distinguish human lncRNAs. We compare our method with the one which has more K-mer features and results show that our method has higher area under the curve up to 99.7905%. Conclusions We develop an accurate and efficient method which has novel information entropy features to analyze and classify lncRNAs. Our method is also extendable for research on the other functional elements in DNA sequences.

scholarly journals Use of Machine Learning to Automate the Identification of Basketball Strategies Using Whole Team Player Tracking Data

2019 ◽  
Vol 10 (1) ◽  
pp. 24 ◽  
Author(s):  
Changjia Tian ◽  
Varuna De Silva ◽  
Michael Caine ◽  
Steve Swanson
Keyword(s):  
Machine Learning ◽  
Team Sports ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Tracking Data ◽  
Defensive Strategy ◽  
Defensive Strategies ◽  
Player Tracking ◽  
Ball Tracking ◽  
The One

The use of machine learning to identify and classify offensive and defensive strategies in team sports through spatio-temporal tracking data has received significant interest recently in the literature and the global sport industry. This paper focuses on data-driven defensive strategy learning in basketball. Most research to date on basketball strategy learning has focused on offensive effectiveness and is based on the interaction between the on-ball player and principle on-ball defender, thereby ignoring the contribution of the remaining players. Furthermore, most sports analytical systems that provide play-by-play data is heavily biased towards offensive metrics such as passes, dribbles, and shots. The aim of the current study was to use machine learning to classify the different defensive strategies basketball players adopt when deviating from their initial defensive action. An analytical model was developed to recognise the one-on-one (matched) relationships of the players, which is utilised to automatically identify any change of defensive strategy. A classification model is developed based on a player and ball tracking dataset from National Basketball Association (NBA) game play to classify the adopted defensive strategy against pick-and-roll play. The methodology described is the first to analyse the defensive strategy of all in-game players (both on-ball players and off-ball players). The cross-validation results indicate that the proposed technique for automatic defensive strategy identification can achieve up to 69% accuracy of classification. Machine learning techniques, such as the one adopted here, have the potential to enable a deeper understanding of player decision making and defensive game strategies in basketball and other sports, by leveraging the player and ball tracking data.

scholarly journals Cloud-Based Parkinson’s Disease Diagnosis Using Machine Learning

2021 ◽  
Vol 8 (6) ◽  
pp. 915-922
Author(s):  
Ahmed R. Nasser ◽  
Ali M. Mahmood
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Treatment ◽  
Intelligent System ◽  
Feature Selection Method ◽  
Disease Diagnosis ◽  
Classification Model ◽  
Accuracy Measurement ◽  
Diagnosis Model

Parkinson’s disease (PD) harms the human brain's nervous system and can affect the patient's life. However, the diagnosis of PD diagnosis in the first stages can lead to early treatment and save costs. In this paper, a cloud-based machine learning diagnosing intelligent system is proposed for the PD with respect to patient voice. The proposed system is composed of two stages. In the first stage, two machine learning approaches, Random-Forest (RF) and Long-Short-Term-Memory (LSTM) are applied to generate a model that can be used for early treatment of PD. In this stage, a feature selection method is used to choose the minimum subset of the best features, which can be utilized later to generate the classification model. In the second stage, the best diagnosis model is deployed in cloud computing. In this stage, an Android application is also designed to provide the interface to the diagnosis model. The performance evaluation of the diagnosis model is conducted based on the F-score accuracy measurement. The result shows that the LTSM model has superior accuracy with 95% of the F-score compared with the RF model. Therefore, the LSTM model is selected for implementing a cloud-based PD diagnosing application using Python and Java.

Verification of a classification prediction method for the development of musical expression in early childhood using a machine learning method based on 3D motion capture data

2020 ◽  
Vol 7 (9) ◽  
pp. 338-358
Author(s):  
Mina Sano
Keyword(s):  
Machine Learning ◽  
Early Childhood ◽  
Statistical Analysis ◽  
Classification Model ◽  
Machine Learning Method ◽  
Learning Method ◽  
Musical Expression ◽  
3D Motion ◽  
Model Training ◽  
Classification Prediction

It is widely acknowledged that children take developmental steps in performing musical expression through body movement dynamics. The author presents an approach to verify the method of classification prediction by machine learning using multiple classifiers to evaluate and classify the developmental degree of musical expressions in early childhood. The author addresses this potential solution by showing statistical analysis of full-body 3D motion captured data using such statistical analysis, and applies machine learning measures to predict developmental degrees of musical expression. In 2016, the author extracted the feature quantities of movement based on the results of the movement analysis of the musical expressions in the MEB program regarding 3-year-old, 4-year-old, and 5-year-old children (n=76). The developmental degree of musical expression was classified into three stages based on video analysis of the musical expression of each child. The 2016 training data as the feature quantity of movement (factors) and the three-stage evaluation (categorical dependent variables) were used as the model training for machine learning. Based on the results in 2017 with 2018, the classification model training results were applied to the acquired data (n=87) in 2019. The result of sensitivity analysis showed that the moving average acceleration of pelvis, the moving distance of right foot and the moving distance of right hand had a strong influence on the development of musical expression in early childhood. From those analysis results, the appropriateness of the machine learning method using decision trees for classifying the developmental degree of musical expression in early childhood was verified.

scholarly journals P.1.11 ‘Creation of classification model using machine learning; to detect dysphonia work-related’

2019 ◽  
Vol 76 (Suppl 1) ◽  
pp. A80.2-A80
Author(s):  
Natalia Gilbert ◽  
Rodrigo Assar ◽  
Rodrigo Martinez
Keyword(s):  
Machine Learning ◽  
Occupational Disease ◽  
Classification Model ◽  
Learning Tools ◽  
Support Tool ◽  
Computer Tools ◽  
Work Related ◽  
Large Databases ◽  
Kappa Value ◽  
The One

The underdiagnosis of occupational disease causes severe damage to the health system. The classification of a disease as a professional is based on the decision on whether the present labor factors are sufficient for the generation of the disease, and this function is carried out by a qualified professional or committee.Occupational dysphonia is one of the 5 most frequent occupational diseases in Chile, whose condition impact on the labor productivity and the quality of life of the patient. Today there are no unified criteria among the occupational qualification decisión makers to decide on the sufficient of laboral factors of occupational dysphonia disease.Computerized systems have been developed to support clinical diagnosis decision-making process; among these, Machine Learning methods have been used to simulate the reasoning of the expert from the analysis and identification of complex patterns in large databases, so in this study it is suggested that the creation of a dysphonia classification model is possible employing Machine Learning tools. For this purpose, 103 cases obtained from patients with qualification results cause by dysphonia was analize in relation to the number of variables studied and their distribution for the observation of the characteristics that give identity to the groups studied. Subsequently, different classification models were developed using Machine Learning and the one that presented the best performance was chosen.Statistical analyzes show that of the 6 models of Machine Learning elaborated, Random Forest was the one that presented the best performance (accuracity=0.83 and Kappa value=0.61), variables that manage to establish identity to each group represent 26.5% of the total of studied variables. The results in this work show the potential of the use of computer tools can be useful as a support tool for diagnosis of occupational disease.

scholarly journals Explainable Machine Learning Prediction for Mortality of COVID-19 in the Colombian Population

2021 ◽  
Author(s):  
Gabriel Ricardo Vásquez Morales ◽  
Sergio Mauricio Martínez Monterrubio ◽  
Juan Antonio Recio García ◽  
Pablo Moreno Ger
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Public Health Problem ◽  
Classification Model ◽  
Global Public Health ◽  
The Neural Network ◽  
Large Numbers ◽  
History Of ◽  
The One ◽  
Global Public Health Problem

Abstract The COVID-19 pandemic, which began in late 2019, has become a global public health problem, resulting in large numbers of people infected and dead. One of the greatest challenges in dealing with the disease is to identify those people who are most at risk of becoming infected, seriously ill and dying from the virus, so that they can be isolated in a targeted manner and thus reduce mortality rates. This article proposes the use of machine learning, and specifically of neural networks and random forest to build two complementary models that identify the probability that a person has of dying because of COVID-19. The models are trained with the demographic information and medical history of two population groups: on the one hand, 43,000 people who died from COVID-19 in Colombia during 2020, and on the other hand, a random sample of 43,000 people who became ill with COVID-19 during the same period of time, but later recovered. After training the neural network classification model, evaluation metrics were applied that yielded an 88% accuracy value. However, transparency is a major requirement for the explicability of COVID-19 prognosis. Therefore, a complementary random forest model is trained that allows the identification of the most significant predictors of mortality by COVID-19.

A Multi-agent Feature Selection and Hybrid Classification Model for Parkinson's Disease Diagnosis

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-22
Author(s):  
Mazin Abed Mohammed ◽  
Mohamed Elhoseny ◽  
Karrar Hameed Abdulkareem ◽  
Salama A. Mostafa ◽  
Mashael S. Maashi
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Prediction Models ◽  
Disease Diagnosis ◽  
Classification Model ◽  
Test Results ◽  
Multi Agent ◽  
The Voice

Parkinson's disease (PD) diagnostics includes numerous analyses related to the neurological, physical, and psychical status of the patient. Medical teams analyze multiple symptoms and patient history considering verified genetic influences. The proposed method investigates the voice symptoms of this disease. The voice files are processed, and the feature extraction is conducted. Several machine learning techniques are used to recognize Parkinson's and healthy patients. This study focuses on examining PD diagnosis through voice data features. A new multi-agent feature filter (MAFT) algorithm is proposed to select the best features from the voice dataset. The MAFT algorithm is designed to select a set of features to improve the overall performance of prediction models and prevent over-fitting possibly due to extreme reduction to the features. Moreover, this algorithm aims to reduce the complexity of the prediction, accelerate the training phase, and build a robust training model. Ten different machine learning methods are then integrated with the MAFT algorithm to form a powerful voice-based PD diagnosis model. Recorded test results of the PD prediction model using the actual and filtered features yielded 86.38% and 86.67% accuracies on average, respectively. With the aid of the MAFT feature selection, the test results are improved by 3.2% considering the hybrid model (HM) and 3.1% considering the Naïve Bayesian and random forest. Subsequently, an HM, which comprises a binary convolutional neural network and three feature selection algorithms (namely, genetic algorithm, Adam optimizer, and mini-batch gradient descent), is proposed to improve the classification accuracy of the PD. The results reveal that PD achieves an overall accuracy of 93.7%. The HM is integrated with the MAFT, and the combination realizes an overall accuracy of 96.9%. These results demonstrate that the combination of the MAFT algorithm and the HM model significantly enhances the PD diagnosis outcomes.

Real-time seismic damage prediction and comparison of various ground motion intensity measures based on machine learning

2021 ◽  
Author(s):  
Yongjia Xu ◽  
Xinzheng Lu ◽  
Yuan Tian ◽  
Yuli Huang
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Case Studies ◽  
Ground Motion ◽  
Emergency Response ◽  
Prediction Method ◽  
Seismic Damage ◽  
Intensity Measures ◽  
Damage Prediction ◽  
Model Training

<p>After earthquakes, an accurate and efficient seismic damage prediction is indispensable for emergency response. Existing methods face the dilemma between accuracy and efficiency. A real-time and accurate seismic damage prediction method based on machine-learning is proposed here. 48 intensity measures are used as input to represent the ground motion comprehensively. Besides, the workload of the NLTHA method is replaced by model training/testing and moved to a non-urgent stage to promote efficiency. Case studies with various building cases prove the accuracy and efficiency of the proposed method. Key intensity measures for each building are identified by iteratively using the proposed framework.</p>

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

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