An Intelligent Forecasting Model for Disease Prediction Using Stack Ensembling Approach

Verma, S. S.; Sharma, Nonita; Singh, Aman; Alharbi, Abdullah; Alosaimi, Wael; Alyami, Hashem; Gupta, Deepali; Goyal, Nitin

doi:10.32604/cmc.2022.021747

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…The diverse models in our study were due to different random splits of the data, randomization of the SMOTE process, and randomization of the initial sets of weights and biases of the ANN models prior to training. Previous studies have used ensemble processes to improve predictions over those made by individually trained models [ 37 , 38 ]. An additional advantage of the ensemble process in our study was that the variability of the predictions for individual patient records could be determined.…”

Section: Discussionmentioning

confidence: 99%

Stone decision engine accurately predicts stone removal and treatment complications for shock wave lithotripsy and laser ureterorenoscopy patients

Noble,

Hamilton,

Gerber

2024

PLoS ONE

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Kidney stones form when mineral salts crystallize in the urinary tract. While most stones exit the body in the urine stream, some can block the ureteropelvic junction or ureters, leading to severe lower back pain, blood in the urine, vomiting, and painful urination. Imaging technologies, such as X-rays or ureterorenoscopy (URS), are typically used to detect kidney stones. Subsequently, these stones are fragmented into smaller pieces using shock wave lithotripsy (SWL) or laser URS. Both treatments yield subtly different patient outcomes. To predict successful stone removal and complication outcomes, Artificial Neural Network models were trained on 15,126 SWL and 2,116 URS patient records. These records include patient metrics like Body Mass Index and age, as well as treatment outcomes obtained using various medical instruments and healthcare professionals. Due to the low number of outcome failures in the data (e.g., treatment complications), Nearest Neighbor and Synthetic Minority Oversampling Technique (SMOTE) models were implemented to improve prediction accuracies. To reduce noise in the predictions, ensemble modeling was employed. The average prediction accuracies based on Confusion Matrices for SWL stone removal and treatment complications were 84.8% and 95.0%, respectively, while those for URS were 89.0% and 92.2%, respectively. The average prediction accuracies for SWL based on Area-Under-the-Curve were 74.7% and 62.9%, respectively, while those for URS were 77.2% and 78.9%, respectively. Taken together, the approach yielded moderate to high accurate predictions, regardless of treatment or outcome. These models were incorporated into a Stone Decision Engine web application (http://peteranoble.com/webapps.html) that suggests the best interventions to healthcare providers based on individual patient metrics.

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Section: Discussionmentioning

confidence: 99%

Stone decision engine accurately predicts stone removal and treatment complications for shock wave lithotripsy and laser ureterorenoscopy patients

Noble,

Hamilton,

Gerber

2024

PLoS ONE

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“…The authors then used random forest classifier to identify the cropping patterns and measured the performance using performance metrics. Verma et al [11] proposed a stack based ensemble model to predict conjunctivitis disease using time series dataset. The author collects 8 years of data from the Health Department of Hong Kong.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Objective Optimization with Artificial Neural Network Based Robust Paddy Yield Prediction Model

Muthukumaran¹,

Geetha²,

Ramaraj³

2023

Intelligent Automation &Amp; Soft Computing

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Agriculture plays a vital role in the food production process that occupies nearly one-third of the total surface of the earth. Rice is propagated from the seeds of paddy and it is a stable food almost used by fifty percent of the total world population. The extensive growth of the human population alarms us to ensure food security and the country should take proper food steps to improve the yield of food grains. This paper concentrates on improving the yield of paddy by predicting the factors that influence the growth of paddy with the help of Evolutionary Computation Techniques. Most of the researchers used to relay on historical records of meteorological parameters to predict the yield of paddy. There is a lack in analyzing the day to day impact of meteorological parameters such as direction of wind, relative humidity, Instant Wind Speed in paddy cultivation. The real time meteorological data collected and analysis the impact of weather parameters from the day of paddy sowing to till the last day of paddy harvesting with regular time series. A Robust Optimized Artificial Neural Network (ROANN) Algorithm with Genetic Algorithm (GA) and Multi Objective Particle Swarm Optimization Algorithm (MOPSO) proposed to predict the factors that to be concentrated by farmers to improve the paddy yield in cultivation. A real time paddy data collected from farmers of Tamilnadu and the meteorological parameters were matched with the cropping pattern of the farmers to construct the database. The input parameters were optimized either by using GA or MOPSO optimization algorithms to reconstruct the database. Reconstructed database optimized by using Artificial Neural Network Back Propagation Algorithm. The reason for improving the growth of paddy was identified using the output of the Neural Network. Performance metrics such as Accuracy, Error Rate etc were used to measure the performance of the proposed algorithm. Comparative analysis made between ANN with GA and ANN with MOPSO to identify the recommendations for improving the paddy yield.

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Automatic categorization of medical documents in Afaan Oromo using ensemble machine learning techniques

Dinsa,

Das,

Abebe

et al. 2024

Discov Appl Sci

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An Intelligent Forecasting Model for Disease Prediction Using Stack Ensembling Approach

Cited by 5 publications

References 19 publications

Stone decision engine accurately predicts stone removal and treatment complications for shock wave lithotripsy and laser ureterorenoscopy patients

Stone decision engine accurately predicts stone removal and treatment complications for shock wave lithotripsy and laser ureterorenoscopy patients

Multi-Objective Optimization with Artificial Neural Network Based Robust Paddy Yield Prediction Model

Automatic categorization of medical documents in Afaan Oromo using ensemble machine learning techniques

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