scholarly journals The new SUMPOT to predict postoperative complications using an Artificial Neural Network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Cosimo Chelazzi ◽  
Gianluca Villa ◽  
Andrea Manno ◽  
Viola Ranfagni ◽  
Eleonora Gemmi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Postoperative Complications ◽  
Classification Accuracy ◽  
Surgical Patients ◽  
Control Group ◽  
Emergency Procedures ◽  
High Dependency ◽  
Artificial Neural ◽  
Testing Set

AbstractAn accurate assessment of preoperative risk may improve use of hospital resources and reduce morbidity and mortality in high-risk surgical patients. This study aims at implementing an automated surgical risk calculator based on Artificial Neural Network technology to identify patients at risk for postoperative complications. We developed the new SUMPOT based on risk factors previously used in other scoring systems and tested it in a cohort of 560 surgical patients undergoing elective or emergency procedures and subsequently admitted to intensive care units, high-dependency units or standard wards. The whole dataset was divided into a training set, to train the predictive model, and a testing set, to assess generalization performance. The effectiveness of the Artificial Neural Network is a measure of the accuracy in detecting those patients who will develop postoperative complications. A total of 560 surgical patients entered the analysis. Among them, 77 patients (13.7%) suffered from one or more postoperative complications (PoCs), while 483 patients (86.3%) did not. The trained Artificial Neural Network returned an average classification accuracy of 90% in the testing set. Specifically, classification accuracy was 90.2% in the control group (46 patients out of 51 were correctly classified) and 88.9% in the PoC group (8 patients out of 9 were correctly classified). The Artificial Neural Network showed good performance in predicting presence/absence of postoperative complications, suggesting its potential value for perioperative management of surgical patients. Further clinical studies are required to confirm its applicability in routine clinical practice.

scholarly journals The New SUMPOT to Predict Postoperative Complications Using an Artificial Neural Network

2021 ◽  
Author(s):  
Cosimo Chelazzi ◽  
Gianluca Villa ◽  
Andrea Manno ◽  
Viola Ranfagni ◽  
Eleonora Gemmi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Postoperative Complications ◽  
Scoring Systems ◽  
Surgical Patients ◽  
Control Group ◽  
Emergency Procedures ◽  
High Dependency ◽  
Artificial Neural ◽  
Testing Set

Abstract An accurate assessment of preoperative risk may improve use of hospital resources and reduce morbidity and mortality inhigh-risk surgical patients. This study aims at implementing an automated surgical risk calculator based on Artificial NeuralNetwork technology to identify patients at risk for postoperative complications. We developed the new SUMPOT based on risk factors previously used in other scoring systems and tested it in a cohortof 560 surgical patients undergoing elective or emergency procedures and subsequently admitted to intensive care units,high-dependency units or standard wards. The whole dataset was divided into a training set, to train the predictive model, anda testing set, to assess generalization performance. The effectiveness of the Artificial Neural Network is a measure of theaccuracy in detecting those patients who will develop postoperative complications.A total of 560 surgical patients entered the analysis. Among them, 77 patients (13.7%) suffered from one or more postoperativecomplications (PoCs), while 483 patients (86.3%) did not. The trained Artificial Neural Network returned an average classificationaccuracy of 90% in the testing set. Specifically, classification accuracy was 90.2% in the control group (46 patients out of 51were correctly classified) and 88.9% in the PoC group (8 patients out of 9 were correctly classified).The Artificial Neural Network showed good performance in predicting presence/absence of postoperative complications,suggesting its potential value for perioperative management of surgical patients. Further clinical studies are required to confirmits applicability in routine clinical practice.

Application of artificial neural network to loan recovery prediction

2016 ◽  
Vol 9 (2) ◽  
pp. 222-238 ◽  
Author(s):  
Amos Olaolu Adewusi ◽  
Tunbosun Biodun Oyedokun ◽  
Mustapha Oyewole Bello
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Interest Rates ◽  
Classification Accuracy ◽  
Learning Algorithm ◽  
Type I ◽  
Ann Model ◽  
Content Type ◽  
Credit Evaluation ◽  
Artificial Neural

Purpose This study assesses the classification accuracy of an artificial neural network (ANN) model. It examines the application of loan recovery probability rather than odds of default as the case with traditional credit evaluation models. Design/methodology/approach Data on 2,300 loans granted over the period 2001-2012 was obtained from the databases of Nigerian commercial banks and primary mortgage institutions. A multilayer feed-forward ANN model with back-propagation learning algorithm was developed having classified the sample into training (38 per cent), testing (41 per cent) and validation (21 per cent) sub-samples. Findings The model exhibits a high overall percentage classification accuracy of 92.6 per cent. It also achieves relatively low misclassification Type I and Type II errors at 6.5 per cent and 8.2 per cent, respectively. Macroeconomic variables such as gross domestic product, inflation and interest rates have the strongest influence on the ANN model classification power. The result of the analysis shows that adopting odds of recovery in ANN classification models can lead to improved loan evaluation. Originality/value The paper is distinct from extant studies in that it presents a new dimension to loan evaluation in Nigerian lending market. To the best knowledge of the authors, the paper is among the first to explore probability of loan recovery as the basis for credit evaluation in the country.

scholarly journals Automation of systematic reviews through artificial neural network modeling and machine learning: A methodological protocol report (Preprint)

2020 ◽  
Author(s):  
Gabriel Ferraz Ferreira Sr ◽  
Marcos Gonçalves Quiles Sr ◽  
Tiago Santana Nazare Sr ◽  
Solange Oliveira Rezende ◽  
Marcelo Demarzo Sr
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Systematic Review ◽  
Artificial Neural Network ◽  
Systematic Reviews ◽  
Selection Process ◽  
Automated System ◽  
Neural Network Modeling ◽  
Control Group ◽  
Artificial Neural

UNSTRUCTURED Background: A systematic review can be defined as a summary of the evidence found in the literature via a systematic search in the available scientific databases. One of the steps involved is article selection, which is typically a laborious task. Machine learning and artificial intelligence can be important tools in automating this step, thus aiding researchers. The aim of this study is to create models based on an artificial neural network system and machine learning to automate the article selection process in systematic reviews in the area of Mindfulness. Methods: The study will be performed using R programming software. The system will consist of six main steps: 1) data import; 2) exclusion of duplicates; 3) exclusion of nonarticles; 4) article reading and model creation using artificial neural networks; 5) comparison of the models; and 6) system sharing. We will choose the 10 most relevant systematic reviews published in the fields of “Mindfulness and Health Promotion” and “Orthopedics and Traumatology” (control group) to serve as a test of the effectiveness of the article selection. The final results for these two fields will be compared. Conclusion: An automated system with a modifiable sensitivity will be created to select scientific articles in systematic review that can be expanded to various fields. We will disseminate our results and models through the “Observatory of Evidence” in public health, an open and online platform that will assist researchers in systematic reviews.

scholarly journals Fault Classification Accuracy Measurement for a Distribution System with Artificial Neural Network without using Signal Processing Technique

2020 ◽  
Vol 9 (3) ◽  
pp. 1523-1527 ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Signal Processing ◽  
Classification Accuracy ◽  
Distribution Network ◽  
Processing Method ◽  
Fault Classification ◽  
Accuracy Measurement ◽  
Signal Processing Method ◽  
Artificial Neural

Faults occurring on electrical distribution network are unpredictable and needs to be cleared at the earliest so as to reduce power outage time. Hence fault detection and their classification plays important role. In this research paper the fault classification accuracy was measured for an electrical power distribution network with artificial neural network without using any signal processing method. Although many digital signal processing methods are developed to enhance electrical fault classification accuracy, it is essential to measure it for comparison when no signal processing method is used. Fault classification was considered as a pattern recognition application of neural networks. Two layer feed forward back propagation neural network was used as classifier. IEEE 13 bus distribution feeder was simulated in MATLAB with Simulink for collecting the input data. The simulation results show that the faults can be classified satisfactorily. L-G, L-L and L-L-L faults were simulated for measuring the accuracy of fault classification.

scholarly journals DETERMINATION OF THE OPTIMUM MATURITY OF NEW RICE FOR AFRICA USING ARTIFICIAL NEURAL NETWORK

2021 ◽  
Vol 5 (1) ◽  
pp. 502-510
Author(s):  
A. Peter
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Classification Accuracy ◽  
Food Production ◽  
Rice Variety ◽  
Maturity Period ◽  
Artificial Neural ◽  
Visual Properties ◽  
Agricultural Food

The New Rice for Africa (NERICA) is a child birth of research to improve upon the production of rice in sub-Sahara Africa due to challenges of shortages in agricultural food production. Two major varieties were obtained, for low lands and uplands. NERICA-4 is commonly suited for uplands and has delicious taste as compared to the other upland varieties. However, the problem of loss of grains at harvest which translates to low productivity amongst other factors needs to be addressed. In this paper, about 750m2 farm land was cultivated with NERICA-4 rice variety and 60 images at different maturity period with ten features extracted, preprocessed and processed using MATLAB2018Ra software. The processed images were classified using Artificial Neural Network to determine the optimum maturity period based on visual properties. 93.30% classification accuracy was obtained. This shows that when made operational, the loss of grains can be drastically reduced and productivity increased

BRIGHTNESS PRESERVING HISTOGRAM EQUALIZATION (BPHE) TECHNIQUE FOR CLASSIFICATION OF DATES

2013 ◽  
pp. 200-202
Author(s):  
MANICKAVASAGAN. A ◽  
GABRIEL THOMAS ◽  
AL-YAHYAI, R ◽  
HEMA, M
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Classification Accuracy ◽  
Histogram Equalization ◽  
Neural Network Classifier ◽  
Artificial Neural Network Classifier ◽  
Artificial Neural ◽  
Hidden Neurons ◽  
Classification Efficiency

Brightness preserving histogram equalization (BPHE) technique was used to enhance the features to discriminate three dates varieties (Khalas, Fard and Madina). Mean, entropy and kurtosis features were computed from the enhanced images and used in an Artificial Neural Network classifier. The classification efficiency of 4 sets of hidden neurons (5, 10, 20, and 30) was tested and the network with 5 neurons yielded the highest classification accuracy of 95.2%.

scholarly journals Artificial Neural Network Classification of Motor-Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Vladimir A. Maksimenko ◽  
Semen A. Kurkin ◽  
Elena N. Pitsik ◽  
Vyacheslav Yu. Musatov ◽  
Anastasia E. Runnova ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Motor Imagery ◽  
High Frequency ◽  
Classification Accuracy ◽  
Eeg Signals ◽  
Pass Filter ◽  
Time Frequency ◽  
Artificial Neural

We apply artificial neural network (ANN) for recognition and classification of electroencephalographic (EEG) patterns associated with motor imagery in untrained subjects. Classification accuracy is optimized by reducing complexity of input experimental data. From multichannel EEG recorded by the set of 31 electrodes arranged according to extended international 10-10 system, we select an appropriate type of ANN which reaches 80 ± 10% accuracy for single trial classification. Then, we reduce the number of the EEG channels and obtain an appropriate recognition quality (up to 73 ± 15%) using only 8 electrodes located in frontal lobe. Finally, we analyze the time-frequency structure of EEG signals and find that motor-related features associated with left and right leg motor imagery are more pronounced in the mu (8–13 Hz) and delta (1–5 Hz) brainwaves than in the high-frequency beta brainwave (15–30 Hz). Based on the obtained results, we propose further ANN optimization by preprocessing the EEG signals with a low-pass filter with different cutoffs. We demonstrate that the filtration of high-frequency spectral components significantly enhances the classification performance (up to 90 ± 5% accuracy using 8 electrodes only). The obtained results are of particular interest for the development of brain-computer interfaces for untrained subjects.

Comparison of Machining Performances in Wire EDM for HCHCr Material Using Group Method Data Handling Technique and Artificial Neural Network

2015 ◽  
Author(s):  
Ugrasen Gonchikar ◽  
Ravindra Holalu Venkatadas ◽  
Naveen Prakash Goravi Vijaya Dev ◽  
Keshavamurthy Ramaiah ◽  
Giridhara Gudekota
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Group Method ◽  
Data Handling ◽  
Training Set ◽  
Work Material ◽  
Handling Technique ◽  
Artificial Neural ◽  
Group Method Data Handling ◽  
Testing Set

Wire Electrical Discharge Machining (WEDM) is a specialized thermo electrical machining process capable of accurately machining parts with varying hardness or complex shapes. Present study outlines the comparison of machining performances in the wire electric discharge machining using group method data handling technique and artificial neural network. HCHCr material was selected as a work material. This work material was machined using different process parameters based on Taguchi’s L27 standard orthogonal array. Parameters such as pulse-on time, pulse-off time, current and bed speed were varied. The response variables measured for the analysis are surface roughness, volumetric material removal rate and dimensional error. Machining performances were compared using sophisticated mathematical models viz., Group Method of Data Handling (GMDH) technique and Artificial Neural Network (ANN). GMDH is ideal for complex, unstructured systems where the investigator is only interested in obtaining a high-order input-output relationship. Also, the method is heuristic in nature and is not based on a solid foundation as regression analysis. The GMDH algorithm is designed to learn the process by training the algorithm with the experimental data. The experimental observations are divided into two sets viz., the training set and testing set. The training set is used to make the GMDH learn the process and the testing set will check the performance of GMDH. Different models were obtained by varying the percentage of data in the training set and the best model were selected from these, viz., 50%, 62.5% & 75%. The best model was selected from the said percentages of data. Number of variables selected at each layer is usually taken as a fixed number or a constantly increasing number. It is usually given as fractional increase in number of independent variables present in the previous level. Three different criterion functions, viz., Root Mean Square (Regularity) criterion, Unbiased criterion and Combined criterion were considered for estimation. The choice of the criterion for node selection is another important parameter for proper modeling. The Artificial Neural Network is used to study and predict the machining responses. Input data are fed into the neural network and corresponding weights and bias are extracted. Then weights and bias are integrated in the program which is used to calculate and predict the machining responses. Estimation of machining performances was obtained by using ANN for various cutting conditions. ANN estimates were obtained for various percentages of total data in the training set viz., 50%, 60% & 70%. The best model was selected from the said percentages of data. Estimation and comparison of machining performances were carried out using GMDH and ANN. Estimates from GMDH and ANN were compared and it was observed that ANN with 70% of data in training set gives better results than GMDH.

scholarly journals An Enhanced Machine Learning Framework for Type 2 Diabetes Classification Using Imbalanced Data with Missing Values

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Kumarmangal Roy ◽  
Muneer Ahmad ◽  
Kinza Waqar ◽  
Kirthanaah Priyaah ◽  
Jamel Nebhen ◽  
...  
Keyword(s):  
Neural Network ◽  
Type 2 Diabetes ◽  
Artificial Neural Network ◽  
Classification Accuracy ◽  
Missing Values ◽  
Metabolic Diseases ◽  
Population Sample ◽  
K Nearest Neighbor ◽  
Artificial Neural

Diabetes is one of the most common metabolic diseases that cause high blood sugar. Early diagnosis of such a condition is challenging due to its complex interdependence on various factors. There is a need to develop critical decision support systems to assist medical practitioners in the diagnosis process. This research proposes developing a predictive model that can achieve a high classification accuracy of type 2 diabetes. The study consisted of two fundamental parts. Firstly, the study investigated handling missing data adopting data imputation, namely, median value imputation, K-nearest neighbor imputation, and iterative imputation. Consequently, the study validated the implications of these imputations using various classification algorithms, i.e., linear, tree-based, and ensemble algorithms, to see how each method affected classification accuracy. Secondly, Artificial Neural Network was employed to model the best performing imputed data, balanced with SMOTETomek ensuring each class is represented fairly. This approach provided the best accuracy of 98% on the test data, outperforming accuracies achieved in prior studies using the same dataset. The dataset used in this study is concerned with gender and population. As a prospect, the study recommends adopting a larger population sample without geographic boundaries. Additionally, as the developed Artificial Neural Network model did not undergo any specific hyperparameter tuning, it would be interesting to explore tuning on top of normalized data to optimize accuracy further.

Using Functional Link Artificial Neural Network (FLANN) for Bank Credit Risk Assessment

2020 ◽  
pp. 1200-1222
Author(s):  
Saroj Kanta Jena ◽  
Maheshwar Dwivedy ◽  
Anil Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Service Provider ◽  
Classification Accuracy ◽  
Credit Scoring ◽  
Data Mining Technique ◽  
Functional Link ◽  
Proposed Model ◽  
Artificial Neural ◽  
Banking Credit

Credit scoring is the most important and critical component conducted by the credit providers to decide whether to grant a loan to the applicant or not. Therefore credit scoring models are generally used to predict the potentiality of the loan applicant. A proper evaluation of the credit can help the service provider to determine whether to grant or to reject the credit. The objective of the study is to predict banking credit scoring assessment using a data mining technique i.e. Functional Link Artificial Neural Network (FLANN) classifier. Credit approval datasets: Australian credit and German credit have been used to do this analysis. The output of the study shows that the proposed model used for classification works better on credit dataset. Secondly, we have applied our proposed model on the two credit approval dataset to check the performance of the model for the classification accuracy. A proper evaluation of the credit using the proposed FLANN approach can help the service provider to accurately and quickly ascertain whether to grant credit or to reject.

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