Hyperparameter Tuning and Optimization in Machine Learning for Species Identification System

Pillai, Sofia K.; Raghuwanshi, M. M.; Gaikwad, Mihir J.

doi:10.1007/978-981-15-3020-3_22

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…For optimal performance of the presented models, a search was performed on a grid of chosen parameters to obtain a set of best-performing parameters. The grid search was implemented using the GridSearchCV() function from the scikit-learn library [ 42 , 81 , 82 ]. The optimization and choice of hyperparameters is also based on different techniques such as optimization for genetic algorithms, Bayesian optimization and for machine learning [ 83 , 84 ].…”

Section: Prediction Of Health Status In Patients Infected By Sars-cov-2mentioning

confidence: 99%

Mortality Analysis of Patients with COVID-19 in Mexico Based on Risk Factors Applying Machine Learning Techniques

et al. 2022

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The new pandemic caused by the COVID-19 virus has generated an overload in the quality of medical care in clinical centers around the world. Causes that originate this fact include lack of medical personnel, infrastructure, medicines, among others. The rapid and exponential increase in the number of patients infected by COVID-19 has required an efficient and speedy prediction of possible infections and their consequences with the purpose of reducing the health care quality overload. Therefore, intelligent models are developed and employed to support medical personnel, allowing them to give a more effective diagnosis about the health status of patients infected by COVID-19. This paper aims to propose an alternative algorithmic analysis for predicting the health status of patients infected with COVID-19 in Mexico. Different prediction models such as KNN, logistic regression, random forests, ANN and majority vote were evaluated and compared. The models use risk factors as variables to predict the mortality of patients from COVID-19. The most successful scheme is the proposed ANN-based model, which obtained an accuracy of 90% and an F1 score of 89.64%. Data analysis reveals that pneumonia, advanced age and intubation requirement are the risk factors with the greatest influence on death caused by virus in Mexico.

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Section: Prediction Of Health Status In Patients Infected By Sars-cov-2mentioning

confidence: 99%

Mortality Analysis of Patients with COVID-19 in Mexico Based on Risk Factors Applying Machine Learning Techniques

et al. 2022

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“…We implemented the grid search using the GridSearchCV() function from Sklearn library. Table 2 presents the hyperparameters as returned by the grid search algorithm ( 30 ).…”

Section: Discussionmentioning

confidence: 99%

COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm

Iwendi

Bashir

Peshkar

et al. 2020

Front. Public Health

403

244

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Integration of artificial intelligence (AI) techniques in wireless infrastructure, real-time collection, and processing of end-user devices is now in high demand. It is now superlative to use AI to detect and predict pandemics of a colossal nature. The Coronavirus disease 2019 (COVID-19) pandemic, which originated in Wuhan China, has had disastrous effects on the global community and has overburdened advanced healthcare systems throughout the world. Globally; over 4,063,525 confirmed cases and 282,244 deaths have been recorded as of 11th May 2020, according to the European Centre for Disease Prevention and Control agency. However, the current rapid and exponential rise in the number of patients has necessitated efficient and quick prediction of the possible outcome of an infected patient for appropriate treatment using AI techniques. This paper proposes a fine-tuned Random Forest model boosted by the AdaBoost algorithm. The model uses the COVID-19 patient's geographical, travel, health, and demographic data to predict the severity of the case and the possible outcome, recovery, or death. The model has an accuracy of 94% and a F1 Score of 0.86 on the dataset used. The data analysis reveals a positive correlation between patients' gender and deaths, and also indicates that the majority of patients are aged between 20 and 70 years.

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