Identification of fluorescently-barcoded nanoparticles using machine learning

Ortiz‐Perez, Ana; Lozano, Cristina Izquierdo; Meijers, Rens; Grisoni, Francesca; Albertazzi, Lorenzo

doi:10.1039/d2na00648k

Cited by 1 publication

(1 citation statement)

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“…PyCaret has various performance metrics embedded that allowed the authors to compare the various algorithms, including a confusion matrix, class prediction error, and precision-recall curve [8]. Post-data preprocessing enables significantly better results within the model evaluation step and leads to model tuning, which further refines the best-selected model and prepares it for prediction analysis [46]. The prediction function best operates after the best-selected model undergoes tuning and then proceeds to test the given model with the test data that split after data preprocessing.…”

Section: Details Of the Selected Automl Toolsmentioning

confidence: 99%

Evaluating the Performance of Automated Machine Learning (AutoML) Tools for Heart Disease Diagnosis and Prediction

Paladino,

Hughes,

Perera

et al. 2023

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Globally, over 17 million people annually die from cardiovascular diseases, with heart disease being the leading cause of mortality in the United States. The ever-increasing volume of data related to heart disease opens up possibilities for employing machine learning (ML) techniques in diagnosing and predicting heart conditions. While applying ML demands a certain level of computer science expertise—often a barrier for healthcare professionals—automated machine learning (AutoML) tools significantly lower this barrier. They enable users to construct the most effective ML models without in-depth technical knowledge. Despite their potential, there has been a lack of research comparing the performance of different AutoML tools on heart disease data. Addressing this gap, our study evaluates three AutoML tools—PyCaret, AutoGluon, and AutoKeras—against three datasets (Cleveland, Hungarian, and a combined dataset). To evaluate the efficacy of AutoML against conventional machine learning methodologies, we crafted ten machine learning models using the standard practices of exploratory data analysis (EDA), data cleansing, feature engineering, and others, utilizing the sklearn library. Our toolkit included an array of models—logistic regression, support vector machines, decision trees, random forest, and various ensemble models. Employing 5-fold cross-validation, these traditionally developed models demonstrated accuracy rates spanning from 55% to 60%. This performance is markedly inferior to that of AutoML tools, indicating the latter’s superior capability in generating predictive models. Among AutoML tools, AutoGluon emerged as the superior tool, consistently achieving accuracy rates between 78% and 86% across the datasets. PyCaret’s performance varied, with accuracy rates from 65% to 83%, indicating a dependency on the nature of the dataset. AutoKeras showed the most fluctuation in performance, with accuracies ranging from 54% to 83%. Our findings suggest that AutoML tools can simplify the generation of robust ML models that potentially surpass those crafted through traditional ML methodologies. However, we must also consider the limitations of AutoML tools and explore strategies to overcome them. The successful deployment of high-performance ML models designed via AutoML could revolutionize the treatment and prevention of heart disease globally, significantly impacting patient care.

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Section: Details Of the Selected Automl Toolsmentioning

confidence: 99%