An overview of neural networks

Analysis and prediction of older adult sports participation in South Korea using artificial neural networks and logistic regression models

BACKGROUND

Korea's aging population and the lack of older adult participation in sports are increasing medical expenses.

AIMS

This study aimed to segment older adult sports participants based on their demographic characteristics and exercise practice behavior and applied artificial neural network and logistic regression models to these segments to best predict the effect of medical cost reduction. It presents strategies for older adult sports participation.

METHODS

A sample comprising data on 1,770 older adults aged 50 years and above was drawn from the 2019 National Sports Survey. The data were analyzed through frequency analysis, hierarchical and K-means clustering, artificial neural network, logistic regression, cross-tabulation analyses, and one-way ANOVA using SPSS 23 and Modeler 14.2.

RESULTS

The participants were divided into five clusters. The artificial neural network and logistic analysis models showed that the cluster comprising married women in their 60s who participated in active exercise had the highest possibility of reducing medical expenses.

DISCUSSION

Targeting women in their 60s who actively participate in sports, the government should expand the supply of local gymnasiums, community centers, and sports programs. If local gymnasiums and community centers run sports programs and appoint appropriate sports instructors, the most effective medical cost reduction effect can be obtained.

CONCLUSIONS

This study contributes to the field by providing insights into the specific demographic segments to focus on for measures to reduce medical costs through sports participation.

Automated guided vehicles with a mounted serial manipulator: A systematic literature review

Automated Guided Vehicles (AGVs) have become a vital part of the automation sector and a key component of a new industrial revolution that promises to: i. automate the entire manufacturing process, ii. increase productivity rates, iii. develop safer workplaces, while iv. maximising profits and reducing running costs for businesses. However, several concerns arise in the face of this very promising revolution. A major issue is how to ensure that AGVs function effectively and safely during interactions with humans. Another one concerns the ethical desirability of pervasive, continuous, and multidimensional couplings (or interactions) between humans and robots. Generally speaking, automated systems, in virtue of their vast sensing capabilities, may pose privacy challenges to their users. This is because such systems can seamlessly gather information about people' behaviors, without people's consent or awareness. To tackle the important issues abovementioned, we performed a systematic literature review [SLR] on AGVs with mounted serial manipulators. We used as an input 282 papers published in the relevant scientific literature. We analysed these papers and selected 50 papers based on certain criteria to find out trends, algorithms, performance metrics used, as well as potential ethical concerns raised by the deployment of AGVs in the industry. Our findings suggest that corporations can effectively rely on AGVs with mounted manipulators as an efficient and safe solution to production challenges.

Machine Learning to Predict the Progression of Bone Mass Loss Associated with Personal Characteristics and a Metabolic Syndrome Scoring Index

A relationship exists between metabolic syndrome (MetS) and human bone health; however, whether the combination of demographic, lifestyle, and socioeconomic factors that are associated with MetS development also simultaneously affects bone density remains unclear. Using a machine learning approach, the current study aimed to estimate the usefulness of predicting bone mass loss using these potentially related factors. The present study included a sample of 23,497 adults who routinely visited a health screening center at a large health center at least once during each of three 3-year stages (i.e., 2006–2008, 2009–2011, and 2012–2014). The demographic, socioeconomic, lifestyle characteristics, body mass index (BMI), and MetS scoring index recorded during the first 3-year stage were used to predict the subsequent occurrence of osteopenia using a non-concurrence design. A concurrent prediction was also performed using the features recorded from the same 3-year stage as the predicted outcome. Machine learning algorithms, including logistic regression (LR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were applied to build predictive models using a unique feature set. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, precision, and F1 score were used to evaluate the predictive performances of the models. The XGBoost model presented the best predictive performance among the non-concurrence models. This study suggests that the ensemble learning model with a MetS severity score can be used to predict the progression of osteopenia. The inclusion of an individual’s features into a predictive model over time is suggested for future studies.

The Utility of Artificial Neural Networks for the Non-Invasive Prediction of Metabolic Syndrome Based on Personal Characteristics

This study investigated the diagnostic accuracy of using an artificial neural network (ANN) for the prediction of metabolic syndrome (MetS) based on socioeconomic status and lifestyle factors. The data of 27,415 subjects who went through examinations and answered questionnaires during three stages from 2006 to 2014 at a health institute in Taiwan were collected and analyzed. The repeated measurements over time were set as predictive factors and used to train and test an ANN for MetS prediction. Among the subjects, 18.3%, 24.6%, and 30.1% were diagnosed with MetS during the respective three stages. ANN analysis applied with an over-sampling technique performed with an area under the curve (AUC) of up to 0.93 based on different models. The over-sampling technique helped improve prediction performance in terms of sensitivity and F₂ measures. The results indicated that waist circumference, socioeconomic status (SES), and lifestyle factors can be utilized in a non-invasive screening tool to assist health workers in making primary care decisions when MetS is suspected. By predicting the occurrence of MetS, individuals or healthcare professionals can then develop preventive strategies in time, thus enhancing the effectiveness of health promotion.

Prediction and targeting of GPCR oligomer interfaces

GPCR oligomerization has emerged as a hot topic in the GPCR field in the last years. Receptors that are part of these oligomers can influence each other's function, although it is not yet entirely understood how these interactions work. The existence of such a highly complex network of interactions between GPCRs generates the possibility of alternative targets for new therapeutic approaches. However, challenges still exist in the characterization of these complexes, especially at the interface level. Different experimental approaches, such as FRET or BRET, are usually combined to study GPCR oligomer interactions. Computational methods have been applied as a useful tool for retrieving information from GPCR sequences and the few X-ray-resolved oligomeric structures that are accessible, as well as for predicting new and trustworthy GPCR oligomeric interfaces. Machine-learning (ML) approaches have recently helped with some hindrances of other methods. By joining and evaluating multiple structure-, sequence- and co-evolution-based features on the same algorithm, it is possible to dilute the issues of particular structures and residues that arise from the experimental methodology into all-encompassing algorithms capable of accurately predict GPCR-GPCR interfaces. All these methods used as a single or a combined approach provide useful information about GPCR oligomerization and its role in GPCR function and dynamics. Altogether, we present experimental, computational and machine-learning methods used to study oligomers interfaces, as well as strategies that have been used to target these dynamic complexes.

Artificial neural network models for predicting 1-year mortality in elderly patients with intertrochanteric fractures in China

The mortality rate of older patients with intertrochanteric fractures has been increasing with the aging of populations in China. The purpose of this study was: 1) to develop an artificial neural network (ANN) using clinical information to predict the 1-year mortality of elderly patients with intertrochanteric fractures, and 2) to compare the ANN's predictive ability with that of logistic regression models. The ANN model was tested against actual outcomes of an intertrochanteric femoral fracture database in China. The ANN model was generated with eight clinical inputs and a single output. ANN's performance was compared with a logistic regression model created with the same inputs in terms of accuracy, sensitivity, specificity, and discriminability. The study population was composed of 2150 patients (679 males and 1471 females): 1432 in the training group and 718 new patients in the testing group. The ANN model that had eight neurons in the hidden layer had the highest accuracies among the four ANN models: 92.46 and 85.79% in both training and testing datasets, respectively. The areas under the receiver operating characteristic curves of the automatically selected ANN model for both datasets were 0.901 (95%CI=0.814-0.988) and 0.869 (95%CI=0.748-0.990), higher than the 0.745 (95%CI=0.612-0.879) and 0.728 (95%CI=0.595-0.862) of the logistic regression model. The ANN model can be used for predicting 1-year mortality in elderly patients with intertrochanteric fractures. It outperformed a logistic regression on multiple performance measures when given the same variables.

Artificial neural network models for prediction of cardiovascular autonomic dysfunction in general Chinese population

BACKGROUND

The present study aimed to develop an artificial neural network (ANN) based prediction model for cardiovascular autonomic (CA) dysfunction in the general population.

METHODS

We analyzed a previous dataset based on a population sample consisted of 2,092 individuals aged 30-80 years. The prediction models were derived from an exploratory set using ANN analysis. Performances of these prediction models were evaluated in the validation set.

RESULTS

Univariate analysis indicated that 14 risk factors showed statistically significant association with CA dysfunction (P < 0.05). The mean area under the receiver-operating curve was 0.762 (95% CI 0.732-0.793) for prediction model developed using ANN analysis. The mean sensitivity, specificity, positive and negative predictive values were similar in the prediction models was 0.751, 0.665, 0.330 and 0.924, respectively. All HL statistics were less than 15.0.

CONCLUSION

ANN is an effective tool for developing prediction models with high value for predicting CA dysfunction among the general population.

Comparison of artificial neural network and logistic regression models for predicting mortality in elderly patients with hip fracture

PURPOSE

Older patients with hip fracture have a mortality rate one year after surgery of 20-30%. The purpose of this study is to establish a predictive model to assess the outcome of surgical treatment in older patients with hip fracture.

METHODS

A database of information from 286 consecutive cases of surgery for hip fracture from the Department of Orthopedics, National Taiwan University Hospital Yun-Lin Branch, was utilised for model building and testing. Both logistic regression and artificial neural network (ANN) models were developed. Cases were randomly assigned to training and testing datasets. A testing dataset was utilised to test the accuracy of both models (n=89).

RESULTS

The areas under the receiver operator characteristic curves of both models were utilised to compare predictability and accuracy. The logistic regression training and testing datasets had an area of 0.938 (95% CI: 0.904, 0.972) and 0.784 (95% CI: 0.669, 0.899), respectively, below the 0.998 (95% CI: 0.995, 1.000) and 0.949 (95% CI: 0.857, 1.000) of the final ANN model.

CONCLUSION

Overall, ANNs have higher predictive ability than logistic regression, perhaps because they are not affected by interactions between factors. They may assist in complex decision making in the clinical setting.

Exhaled air temperature in asthmatic children: a mathematical evaluation

Recently, the exhaled breath temperature has been proposed as a potential marker for the evaluation of airway inflammation in asthma. The purpose of this study was to verify the ability to distinguish asthmatics from normal controls by a dedicated detailed mathematical evaluation of the exhaled air curve. Analysis was performed in the different phases of the curve of exhaled temperature, i.e. the rate of temperature increase (Delta e degrees T) and the mean plateau value. Principal components analysis (PCA) and artificial neural networks (ANNs) were used for the evaluation of the data in 90 asthmatic children and in 33 healthy age-matched controls. Both PCA and ANNs showed that a separation between patients and controls can be obtained only by the evaluation of the plateau phase of the curve, which better reflects the periphery of the airway.

Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data

Background

In recent years, outcome prediction models using artificial neural network and multivariable logistic regression analysis have been developed in many areas of health care research. Both these methods have advantages and disadvantages. In this study we have compared the performance of artificial neural network and multivariable logistic regression models, in prediction of outcomes in head trauma and studied the reproducibility of the findings.

Methods

1000 Logistic regression and ANN models based on initial clinical data related to the GCS, tracheal intubation status, age, systolic blood pressure, respiratory rate, pulse rate, injury severity score and the outcome of 1271 mainly head injured patients were compared in this study. For each of one thousand pairs of ANN and logistic models, the area under the receiver operating characteristic (ROC) curves, Hosmer-Lemeshow (HL) statistics and accuracy rate were calculated and compared using paired T-tests.

Results

ANN significantly outperformed logistic models in both fields of discrimination and calibration but under performed in accuracy. In 77.8% of cases the area under the ROC curves and in 56.4% of cases the HL statistics for the neural network model were superior to that for the logistic model. In 68% of cases the accuracy of the logistic model was superior to the neural network model.

Conclusions

ANN significantly outperformed the logistic models in both fields of discrimination and calibration but lagged behind in accuracy. This study clearly showed that any single comparison between these two models might not reliably represent the true end results. External validation of the designed models, using larger databases with different rates of outcomes is necessary to get an accurate measure of performance outside the development population.

Development of a model for prediction of survival in pediatric trauma patients: comparison of artificial neural networks and logistic regression

BACKGROUND/PURPOSE

There is a paucity of outcome prediction models for injured children. Using the National Pediatric Trauma Registry (NPTR), the authors developed an artificial neural network (ANN) to predict pediatric trauma death and compared it with logistic regression (LR).

METHODS

Patients in the NPTR from 1996 through 1999 were included. Models were generated using LR and ANN. A data search engine was used to generate the ANN with the best fit for the data. Input variables included anatomic and physiologic characteristics. There was a single output variable: probability of death. Assessment of the models was for both discrimination (ROC area under the curve) and calibration (Lemeshow-Hosmer C-Statistic).

RESULTS

There were 35,385 patients. The average age was 8.1 +/- 5.1 years, and there were 1,047 deaths (3.0%). Both modeling systems gave excellent discrimination (ROC A(z): LR = 0.964, ANN = 0.961). However, LR had only fair calibration, whereas the ANN model had excellent calibration (L/H C stat: LR = 36, ANN = 10.5).

CONCLUSIONS

The authors were able to develop an ANN model for the prediction of pediatric trauma death, which yielded excellent discrimination and calibration exceeding that of logistic regression. This model can be used by trauma centers to benchmark their performance in treating the pediatric trauma population.

An artificial neural network as a model for prediction of survival in trauma patients: validation for a regional trauma area

BACKGROUND

To develop and validate an artificial neural network (ANN) for predicting survival of trauma patients based on standard prehospital variables, emergency room admission variables, and Injury Severity Score (ISS) using data derived from a regional area trauma system, and to compare this model with known trauma scoring systems.

PATIENT POPULATION

The study was composed of 10,609 patients admitted to 24 hospitals comprising a seven-county suburban/rural trauma region adjacent to a major metropolitan area. The data was generated as part of the New York State trauma registry. Study period was from January 1993 through December 1996 (1993-1994: 5,168 patients; 1995: 2,768 patients; 1996: 2,673 patients).

METHODS

A standard feed-forward back-propagation neural network was developed using Glasgow Coma Scale, systolic blood pressure, heart rate, respiratory rate, temperature, hematocrit, age, sex, intubation status, ICD-9-CM Injury E-code, and ISS as input variables. The network had a single layer of hidden nodes. Initial network development of the model was performed on the 1993-1994 data. Subsequent models were generated using the 1993, 1994, and 1995 data. The model was tested first on the 1995 and then on the 1996 data. The ANN model was tested against Trauma and Injury Severity Score (TRISS) and ISS using the receiver operator characteristic (ROC) area under the curve [ROC-A(z)], Lemeshow-Hosmer C-statistic, and calibration curves.

RESULTS

The ANN showed good clustering of the data, with good separation of nonsurvivors and survivors. The ROCA(z) was 0.912 for the ANN, 0.895 for TRISS, and 0.766 for ISS. The ANN exceeded TRISS with respect to calibration (Lemeshow-Hosmer C-statistic: 7.4 for ANN; 17.1 for TRISS). The prediction of survivors was good for both models. The ANN exceeded TRISS in nonsurvivor prediction.

CONCLUSION

An ANN developed for trauma patients using prehospital, emergency room admission data, and ISS gave good prediction of survival. It was accurate and had excellent calibration. This study expands our previous results developed at a single Level I trauma center and shows that an ANN model for predicting trauma deaths can be applied across hospitals with good results

Neural networks: a new technique for development of decision support systems in dentistry

OBJECTIVES

To outline the key concepts of neural network based systems and to evaluate the potential applications of such systems in dentistry.

DATA SOURCES

Published work on neural networks.

CONCLUSIONS

Neural networks may initially seem complex and computer intensive, but actually integrate well with a clinical environment. Neural network expert systems may be trained with only clinical data and as such can be used where 'rule based' decision making is not possible. This is the case in many clinical situations. Neural networks may therefore become important decision making tools within dentistry and have applications both in improving clinical care and in maximizing the cost benefit of care.

Injury severity and probability of survival assessment in trauma patients using a predictive hierarchical network model derived from ICD-9 codes

Accurate assessment of injury severity is critical for decision making related to the prevention, triage, and treatment of injured patients. Presently, the standard method of controlling for variations of injury severity between groups has been based upon the Injury Severity Score (ISS) and the Trauma Score and the Trauma and Injury Severity Score (TRISS) methodology. The purpose of this study was to attempt to build upon previous work using International Classification of Diseases, ninth revision (ICD-9) coded diagnosis, and procedure information available from standard hospital discharge abstracts (UB-82 Billing format) to create a hierarchical network to provide a tool for predicting injury severity and probability of survival.

METHODS

Data were obtained for this analysis from the North Carolina Medical Database. Data were available on all trauma patients admitted to hospitals in North Carolina from January 1, 1988 until June 30, 1992. The dependent variable of interest was the patient's survival after injury, coded as live or die. The independent variables used in the study included the ISS derived using the technique described by MacKenzie Abbreviated Injury Score (AIS) and body system maximum AIS scores, mortality risk ratios derived from the ICD-9-DM primary, secondary, and tertiary diagnoses, primary and secondary procedures as described in previous work, age and gender. Network generation used a commercial software package, AIM (Abtech Corp., Charlottesville, Va.), which is a numeric modeling tool that automatically "learns" knowledge from a data base of examples.

RESULTS

In the test data set an ISS and a prediction of survival based upon the derived network were calculated for each and every patient. The relative predictive power of these two scores were compared by calculating the overall accuracy, sensitivity, and specificity and the false positive and false negative rates. The receiver operator characteristic curves demonstrate that the network is a more effective tool in predicting the outcome of trauma patients. All the measures of predictive power show that the network was the better predictor of outcome than the ISS.

CONCLUSIONS

Given the recognized limitations of the ISS, the widespread availability of the ICD-9 coded diagnoses and procedures, and the availability of many state and regional data bases that have no ISS or Trauma Score, the purpose of this study was to assess the ability of a network derived from limited but widely available hospital discharge data to predict the outcome of injured patients. The study confirms previous work showing that the ICD-9 codes were strongly associated with outcome. The study demonstrated that the network created from these data was a better predictor of outcome than the derived ISS. When the results of the network were compared with other published series, the network, created without access to physiologic information, was almost as accurate, sensitive, and specific as reported values for TRISS and A Severity Characterization of Trauma (ASCOT). Because the present study is the first of its type, further investigations are needed to validate these findings. If other studies corroborate this study, a network model based upon ICD-9 codes could become the principal method for grading injury severity. This would provide superior predictive power of injury severity with important cost savings and universal application.

Data qualification: logic analysis applied toward neural network training

For neural networks to develop good internal representations for pattern mapping, noise in the training set data must be controlled. Because of the many difficulties associated with manually validating training data, we have focused on using decision table techniques as a practical, domain-independent means of optimizing training set formulation. Decision tables provide a variety of mechanisms whereby training set data can be processed to remove ambiguity, contradictions, and other noise. In addition to serving as data filters, decision tables can be used in the evaluation of neural network training.