Data Mining for Healthcare Data: A Comparison of Neural Networks Algorithms

Classification has been considered as an important tool utilized for the extraction of useful information from healthcare dataset. It may be applied for recognition of disease over symptoms. This paper aims to compare and evaluate different approaches of neural networks classification algorithms for healthcare datasets. The algorithms considered here are Multilayer Perceptron, Radial Basis Function, and Voted Perceptron which are tested based on resulted classifiers accuracy, precision, mean absolute error and root mean squared error rates, and classifier training time. All the algorithms are applied for five multivariate healthcare datasets, Echocardiogram, SPECT Heart, Chronic Kidney Disease, Mammographic Mass, and EEG Eye State datasets. Among the three algorithms, this study concludes the best algorithm for the chosen datasets is Multilayer Perceptron. It achieves the highest for all performance parameters tested. It can produce high accuracy classifier model with low error rate, but suffer in training time especially of large dataset. Voted Perceptron performance is the lowest in all parameters tested. For further research, an investigation may be conducted to analyze whether the number of hidden layer in Multilayer Perceptron’s architecture has a significant impact on the training time.

Download Full-text

Stock Market Prediction Performance of Neural Networks: A Literature Review

International Journal of Economics and Finance ◽

10.5539/ijef.v9n11p100 ◽

2017 ◽

Vol 9 (11) ◽

pp. 100 ◽

Cited By ~ 9

Author(s):

Özgür Ican ◽

Taha Bugra Çelik

Keyword(s):

Neural Networks ◽

Prediction Model ◽

Mean Squared Error ◽

Forecast Error ◽

Absolute Error ◽

Absolute Deviation ◽

Error Measures ◽

Squared Error ◽

Number Of Publications

In this paper, previous studies featuring an artificial neural networks based prediction model have been reviewed. The main purpose of this review is to examine studies which use directional prediction accuracy (also known as hit ratio) or profitability of the model as a benchmark since other forecast error measures - namely mean absolute deviation (MAD), root mean squared error (RMSE), mean absolute error (MAE) and mean squared error (MSE) - have been criticized for the argument that they are not able to actually show how useful the prediction model is, in terms of financial gains (i.e. for practical usage). In order to meet the publication selection criteria mentioned above, a large number of publications have been examined and 25 of papers satisfying the criteria are selected for comparison. Classification of the eligible papers are summarized in a table format for future studies.

Download Full-text

Role of Feed Forward Neural Networks Coupled with Genetic Algorithm in Capitalizing of Intracellular Alpha-Galactosidase Production byAcinetobactersp.

BioMed Research International ◽

10.1155/2014/361732 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8 ◽

Cited By ~ 2

Author(s):

Sirisha Edupuganti ◽

Ravichandra Potumarthi ◽

Thadikamala Sathish ◽

Lakshmi Narasu Mangamoori

Keyword(s):

Genetic Algorithm ◽

Neural Networks ◽

Mean Squared Error ◽

Absolute Error ◽

Percentage Error ◽

Feed Forward Neural Network ◽

Feed Forward ◽

Squared Error ◽

Feed Forward Neural Networks ◽

Alpha Galactosidase

Alpha-galactosidase production in submerged fermentation byAcinetobactersp. was optimized using feed forward neural networks and genetic algorithm (FFNN-GA). Six different parameters, pH, temperature, agitation speed, carbon source (raffinose), nitrogen source (tryptone), and K2HPO4, were chosen and used to construct 6-10-1 topology of feed forward neural network to study interactions between fermentation parameters and enzyme yield. The predicted values were further optimized by genetic algorithm (GA). The predictability of neural networks was further analysed by using mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), andR2-value for training and testing data. Using hybrid neural networks and genetic algorithm, alpha-galactosidase production was improved from 7.5 U/mL to 10.2 U/mL.

Download Full-text

Artificial intelligence with deep learning algorithms to model relationships between total tree height and diameter at breast height

Forest Systems ◽

10.5424/fs/2020292-16393 ◽

2020 ◽

Vol 29 (2) ◽

pp. e013

Author(s):

İlker Ercanli

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Mean Squared Error ◽

Tree Height ◽

Absolute Error ◽

Diameter At Breast Height ◽

Root Mean Squared Error ◽

Squared Error ◽

Hidden Layer ◽

Total Tree

Aim of Study: As an innovative prediction technique, Artificial Intelligence technique based on a Deep Learning Algorithm (DLA) with various numbers of neurons and hidden layer alternatives were trained and evaluated to predict the relationships between total tree height (TTH) and diameter at breast height (DBH) with nonlinear least squared (NLS) regression models and nonlinear mixed effect (NLME) regression models.Area of Study: The data of this study were measured from even-aged, pure Turkish Pine (Pinus brutia Ten.) stands in the Kestel Forests located in the Bursa region of northwestern Turkey.Material and Methods: 1132 pairs of TTH-DBH measurements from 132 sample plots were used for modeling relationships between TTH, DBH, and stand attributes such as dominant height (Ho) and diameter (Do).Main Results: The combination of 100 # neurons and 8 # hidden layer in DLA resulted in the best predictive total height prediction values with Average Absolute Error (0.4188), max. Average Absolute Error (3.7598), Root Mean Squared Error (0.6942), Root Mean Squared error % (5.2164), Akaike Information Criteria (-345.4465), Bayesian Information Criterion (-330.836), the average Bias (0.0288) and the average Bias % (0.2166), and fitting abilities with r (0.9842) and Fit Index (0.9684). Also, the results of equivalence tests showed that the DLA technique successfully predicted the TTH in the validation dataset.Research highlights: These superior fitting scores coupled with the validation results in TTH predictions suggested that deep learning network models should be considered an alternative to the traditional nonlinear regression techniques and should be given importance as an innovative prediction technique.Keywords: Prediction; artificial intelligence; deep learning algorithms; number of neurons; hidden layer alternatives.Abbreviations: TTH (total tree height), DBH (diameter at breast height), OLS (ordinary least squares), NLME (nonlinear mixed effect), AIT (Artificial Intelligence Techniques), ANN (Artificial Neural Network), DLA (Deep Learning Algorithm), GPU (Graphical Processing Units), NLS (nonlinear least squared), RMSE (root mean squared error), AIC (Akaike information criteria), BIC (Bayesian information criterion), FI (fit index), AAE (average absolute error), BLUP (best linear unbiased predictor), TOST (two one-sided test method).

Download Full-text

A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran

Sustainability ◽

10.3390/su13147612 ◽

2021 ◽

Vol 13 (14) ◽

pp. 7612

Author(s):

Mahdis sadat Jalaee ◽

Alireza Shakibaei ◽

Amin GhasemiNejad ◽

Sayyed Abdolmajid Jalaee ◽

Reza Derakhshani

Keyword(s):

Hybrid Method ◽

Computational Intelligence ◽

Energy Demand ◽

Mean Squared Error ◽

Absolute Error ◽

Coal Consumption ◽

Squared Error ◽

Artificial Neural ◽

Socio Economic Variables ◽

Intelligence Approach

Coal as a fossil and non-renewable fuel is one of the most valuable energy minerals in the world with the largest volume reserves. Artificial neural networks (ANN), despite being one of the highest breakthroughs in the field of computational intelligence, has some significant disadvantages, such as slow training, susceptibility to falling into a local optimal points, sensitivity of initial weights, and bias. To overcome these shortcomings, this study presents an improved ANN structure, that is optimized by a proposed hybrid method. The aim of this study is to propose a novel hybrid method for predicting coal consumption in Iran based on socio-economic variables using the bat and grey wolf optimization algorithm with an artificial neural network (BGWAN). For this purpose, data from 1981 to 2019 have been used for modelling and testing the method. The available data are partly used to find the optimal or near-optimal values of the weighting parameters (1980–2014) and partly to test the model (2015–2019). The performance of the BGWAN is evaluated by mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), standard deviation error (STD), and correlation coefficient (R^2) between the output of the method and the actual dataset. The result of this study showed that BGWAN performance was excellent and proved its efficiency as a useful and reliable tool for monitoring coal consumption or energy demand in Iran.

Download Full-text

Estimating Physical Activity Energy Expenditure Using an Ensemble Model-Based Patch-Type Sensor Module

Electronics ◽

10.3390/electronics10070861 ◽

2021 ◽

Vol 10 (7) ◽

pp. 861

Author(s):

Kyeung Ho Kang ◽

Mingu Kang ◽

Siho Shin ◽

Jaehyo Jung ◽

Meina Li

Keyword(s):

Physical Activity ◽

Energy Expenditure ◽

Mean Squared Error ◽

Absolute Error ◽

Ensemble Model ◽

Direct Calorimetry ◽

Patch Type ◽

Mortality And Morbidity ◽

Squared Error ◽

Sensor Module

Chronic diseases, such as coronary artery disease and diabetes, are caused by inadequate physical activity and are the leading cause of increasing mortality and morbidity rates. Direct calorimetry by calorie production and indirect calorimetry by energy expenditure (EE) has been regarded as the best method for estimating the physical activity and EE. However, this method is inconvenient, owing to the use of an oxygen respiration measurement mask. In this study, we propose a model that estimates physical activity EE using an ensemble model that combines artificial neural networks and genetic algorithms using the data acquired from patch-type sensors. The proposed ensemble model achieved an accuracy of more than 92% (Root Mean Squared Error (RMSE) = 0.1893, R2 = 0.91, Mean Squared Error (MSE) = 0.014213, Mean Absolute Error (MAE) = 0.14020) by testing various structures through repeated experiments.

Download Full-text

A hybrid recommender system based-on link prediction for movie baskets analysis

Journal Of Big Data ◽

10.1186/s40537-021-00422-0 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Mohammadsadegh Vahidi Farashah ◽

Akbar Etebarian ◽

Reza Azmi ◽

Reza Ebrahimzadeh Dastjerdi

Keyword(s):

Recommender System ◽

Link Prediction ◽

Mean Squared Error ◽

Spatial Clustering ◽

Randomized Algorithm ◽

Similarity Criterion ◽

Absolute Error ◽

Similarity Criteria ◽

Online Systems ◽

Squared Error

AbstractOver the past decade, recommendation systems have been one of the most sought after by various researchers. Basket analysis of online systems’ customers and recommending attractive products (movies) to them is very important. Providing an attractive and favorite movie to the customer will increase the sales rate and ultimately improve the system. Various methods have been proposed so far to analyze customer baskets and offer entertaining movies but each of the proposed methods has challenges, such as lack of accuracy and high error of recommendations. In this paper, a link prediction-based method is used to meet the challenges of other methods. The proposed method in this paper consists of four phases: (1) Running the CBRS that in this phase, all users are clustered using Density-based spatial clustering of applications with noise algorithm (DBScan), and classification of new users using Deep Neural Network (DNN) algorithm. (2) Collaborative Recommender System (CRS) Based on Hybrid Similarity Criterion through which similarities are calculated based on a threshold (lambda) between the new user and the users in the selected category. Similarity criteria are determined based on age, gender, and occupation. The collaborative recommender system extracts users who are the most similar to the new user. Then, the higher-rated movie services are suggested to the new user based on the adjacency matrix. (3) Running improved Friendlink algorithm on the dataset to calculate the similarity between users who are connected through the link. (4) This phase is related to the combination of collaborative recommender system’s output and improved Friendlink algorithm. The results show that the Mean Squared Error (MSE) of the proposed model has decreased respectively 8.59%, 8.67%, 8.45% and 8.15% compared to the basic models such as Naive Bayes, multi-attribute decision tree and randomized algorithm. In addition, Mean Absolute Error (MAE) of the proposed method decreased by 4.5% compared to SVD and approximately 4.4% compared to ApproSVD and Root Mean Squared Error (RMSE) of the proposed method decreased by 6.05 % compared to SVD and approximately 6.02 % compared to ApproSVD.

Download Full-text

Day-Ahead Forecasting of Hourly Photovoltaic Power Based on Robust Multilayer Perception

Sustainability ◽

10.3390/su10124863 ◽

2018 ◽

Vol 10 (12) ◽

pp. 4863 ◽

Cited By ~ 6

Author(s):

Chao Huang ◽

Longpeng Cao ◽

Nanxin Peng ◽

Sijia Li ◽

Jing Zhang ◽

...

Keyword(s):

Power Plants ◽

Mean Squared Error ◽

Absolute Error ◽

Multilayer Perception ◽

Squared Error ◽

The Mean ◽

Effectiveness And Efficiency ◽

Mlp Network ◽

Grid Operation ◽

Better Than

Photovoltaic (PV) modules convert renewable and sustainable solar energy into electricity. However, the uncertainty of PV power production brings challenges for the grid operation. To facilitate the management and scheduling of PV power plants, forecasting is an essential technique. In this paper, a robust multilayer perception (MLP) neural network was developed for day-ahead forecasting of hourly PV power. A generic MLP is usually trained by minimizing the mean squared loss. The mean squared error is sensitive to a few particularly large errors that can lead to a poor estimator. To tackle the problem, the pseudo-Huber loss function, which combines the best properties of squared loss and absolute loss, was adopted in this paper. The effectiveness and efficiency of the proposed method was verified by benchmarking against a generic MLP network with real PV data. Numerical experiments illustrated that the proposed method performed better than the generic MLP network in terms of root mean squared error (RMSE) and mean absolute error (MAE).

Download Full-text

Comparison of ANFIS and FAHP-FGP methods for supplier selection

Kybernetes ◽

10.1108/k-09-2014-0195 ◽

2016 ◽

Vol 45 (3) ◽

pp. 474-489 ◽

Cited By ~ 6

Author(s):

Moloud sadat Asgari ◽

Abbas Abbasi ◽

Moslem Alimohamadlou

Keyword(s):

Performance Measures ◽

Supplier Selection ◽

Mean Squared Error ◽

Selection Process ◽

Evaluation Criteria ◽

Absolute Error ◽

Global Market ◽

Content Type ◽

Squared Error ◽

Fuzzy Delphi

Purpose – In the contemporary global market, supplier selection represents a crucial process for enhancing firms’ competitiveness. This is a multi-criteria decision-making problem that involves consideration of multiple criteria. Therefore this requires reliable methods to select the best suppliers. The purpose of this paper is to examine and propose appropriate method for selecting suppliers. Design/methodology/approach – ANFIS and fuzzy analytic hierarchy process-fuzzy goal programming (FAHP-FGP) are new methods for evaluating and selecting the best suppliers. These methods are used in this study for evaluating suppliers of dairy industries and the results obtained from methods are compared by performance measures such as Mean Squared Error, Root Mean Squared Error, Normalized Root Men Squared Error, Mean Absolute Error, Normalized Root Men Squared Error, Minimum Absolute Error and R2. Findings – The results indicate that the ANFIS method provides better performance compared to the FAHP-FGP method in terms of the selected suppliers scoring higher in all the performance measures. Practical implications – The proposed method could help companies select the best supplier, by avoiding the influence of personal judgment. Originality/value – This study uses the well-structured method of the fuzzy Delphi in order to determine the supplier evaluation criteria as well as the most recent ANFIS and FAHP-FGP methods for supplier selection. In addition, unlike most other studies, it performs the selection process among all available suppliers.

Download Full-text

Software-based simulation for preprocedural assessment of braided stent sizing: a validation study

Journal of Neurosurgery ◽

10.3171/2018.5.jns18976 ◽

2019 ◽

Vol 131 (5) ◽

pp. 1423-1429 ◽

Cited By ~ 2

Author(s):

Krishna Chaitanya Joshi ◽

Ignacio Larrabide ◽

Ahmed Saied ◽

Nada Elsaid ◽

Hector Fernandez ◽

...

Keyword(s):

Intracranial Aneurysms ◽

Mean Squared Error ◽

Absolute Error ◽

Study Data ◽

Optimal Placement ◽

Squared Error ◽

Single Center Study ◽

Measured Length ◽

The Right ◽

Ruptured Intracranial Aneurysms

OBJECTIVEThe authors sought to validate the use of a software-based simulation for preassessment of braided self-expanding stents in the treatment of wide-necked intracranial aneurysms.METHODSThis was a retrospective, observational, single-center study of 13 unruptured and ruptured intracranial aneurysms treated with braided self-expanding stents. Pre- and postprocedural angiographic studies were analyzed. ANKYRAS software was used to compare the following 3 variables: the manufacturer-given nominal length (NL), software-calculated simulated length (SL), and the actual measured length (ML) of the stent. Appropriate statistical methods were used to draw correlations among the 3 lengths.RESULTSIn this study, data obtained in 13 patients treated with braided self-expanding stents were analyzed. Data for the 3 lengths were collected for all patients. Error discrepancy was calculated by mean squared error (NL to ML −22.2; SL to ML −6.14, p < 0.05), mean absolute error (NL to ML 3.88; SL to ML −1.84, p < 0.05), and mean error (NL to ML −3.81; SL to ML −1.22, p < 0.05).CONCLUSIONSThe ML was usually less than the NL given by the manufacturer, indicating significant change in length in most cases. Computational software-based simulation for preassessment of the braided self-expanding stents is a safe and effective way for accurately calculating the change in length to aid in choosing the right-sized stent for optimal placement in complex intracranial vasculature.

Download Full-text

Multiscale Aggregate Networks with Dense Connections for Crowd Counting

Computational Intelligence and Neuroscience ◽

10.1155/2021/9996232 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Pengfei Li ◽

Min Zhang ◽

Jian Wan ◽

Ming Jiang

Keyword(s):

Mean Squared Error ◽

Absolute Error ◽

Image Features ◽

Convolutional Network ◽

Crowd Counting ◽

Squared Error ◽

Crowd Density ◽

Density Maps ◽

Density Map ◽

Map Decoder

The most advanced method for crowd counting uses a fully convolutional network that extracts image features and then generates a crowd density map. However, this process often encounters multiscale and contextual loss problems. To address these problems, we propose a multiscale aggregation network (MANet) that includes a feature extraction encoder (FEE) and a density map decoder (DMD). The FEE uses a cascaded scale pyramid network to extract multiscale features and obtains contextual features through dense connections. The DMD uses deconvolution and fusion operations to generate features containing detailed information. These features can be further converted into high-quality density maps to accurately calculate the number of people in a crowd. An empirical comparison using four mainstream datasets (ShanghaiTech, WorldExpo’10, UCF_CC_50, and SmartCity) shows that the proposed method is more effective in terms of the mean absolute error and mean squared error. The source code is available at https://github.com/lpfworld/MANet.

Download Full-text