Comparing the performance of machine learning algorithms using estimated accuracy

Gupta, Sunil; Saluja, Kamal; Goyal, Ankur; Vajpayee, Amit; Tiwari, Vipin

doi:10.1016/j.measen.2022.100432

Cited by 40 publications

(9 citation statements)

References 6 publications

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“…Gupta et al (10) trabajaron en la comparación del algoritmo de máquina de aprendizaje automático y realizaron un análisis comparativo para verificar la eficiencia de los algoritmos en la minería de datos. Aquí se utilizan dos conjuntos de datos diferentes con una estrategia de validación cruzada de 10, se compara el resultado en base a la matriz de confusión generada por los tres modelos considerados.…”

Section: Discussionunclassified

“…Después de la comparación de varios algoritmos, encontraron que CART presenta mejores resultados según datos utilizados. (10) Osisanwo et al (11) realizaron una investigación de comparación de algoritmos de aprendizaje automático, la comparación se desarrolló utilizando la estadística Kappa que permite observar la precisión.…”

Section: Discussionunclassified

“…Algunos investigadores realizaron un análisis comparativo de la eficiencia de los algoritmos de aprendizaje automático, muchos de ellos utilizaron dos conjuntos de datos diferentes con una estrategia de validación cruzada, compararon los resultados en base a la matriz de confusión generada por los algoritmos. (9) Gupta et al (10) en su investigación "Comparación del rendimiento de los algoritmos de aprendizaje automático mediante la precisión estimada", después de la comparación de varios algoritmos, ha encontrado que CART presenta mejores resultados según datos utilizados. Menciona que el resultado varía según la base de datos, el tamaño, así como otros aspectos.…”

Section: Introductionunclassified

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Comparison of Machine Learning Algorithms for Predicting Patients with Suspected COVID-19

Andrade-Girón

Cisneros

Mejía-Dominguez

et al. 2023

Salud, Ciencia y Tecnología

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The coronavirus disease (COVID-19) outbreak has infected millions of people, causing a high death rate worldwide. Patients suspected of having COVID-19 are transferred to different health facilities, which has caused a saturation in care, for which it is necessary to have a prediction model to classify patients at high risk of clinical deterioration. The objective of the research was to compare classification algorithms based on automatic learning machines, for the prediction of clinical diagnosis in patients with COVID-19. 1000 records of patients with suspected SARS-CoV-2 infection who were admitted by the emergency service in health establishments in Peru were collected. After pre-processing the data and engineering the attributes, a sample of 700 records was determined. Models were designed and algorithms were compared: Logistic Regression, Support Vector Machine, Nearest Neighbors, Decision Tree, Random Forest, and Navie Bayes. The evaluation of the results of each algorithm was carried out using Accuracy, precision, sensitivity and Chohen's Kappa to know the degree of agreement between the prediction by the learning machine and the results of reality, that is, to what extent both results agree in their measurement. The algorithm that presented the best results was the Support Vector Machine and Random Forest, which predicted the patients with an accuracy of 97%, and Cohen's Kappa of 0.95, with figures higher than the other models evaluated.

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Section: Discussionunclassified

Section: Introductionunclassified

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Comparison of Machine Learning Algorithms for Predicting Patients with Suspected COVID-19

Andrade-Girón

Cisneros

Mejía-Dominguez

et al. 2023

Salud, Ciencia y Tecnología

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“…The lack of practical support for psychological problems can also affect an employee's productivity, absence, work commitment, and self-confidence [4]. Using machine learning techniques to predict mental health issues is considered a promising approach for early prevention of the issues mentioned [5]. The research is being conducted for using these methods to predict employees' perceiving of mental health support in the tech industry and ascertaining related factors.…”

Section: Introduction and Methodsmentioning

confidence: 99%

Prediction of Mental Health Support of Employee Perceiving by Using Machine Learning Methods

Jamalirad

Jajroudi

2023

Caring Is Sharing – Exploiting the Value in Data for Health and Innovation

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Employees’ mental health addresses concerns in the technology industry phenomenon. Machine Learning (ML) approaches show promise in predicting mental health problems and identifying related factors. This study used three machine learning models on OSMI 2019 dataset: MLP, SVM, and Decision Tree. Five features are extracted by permutation ML’s method on the dataset. The results indicate that the models have been reasonably accurate. Moreover, they could effectively support predicting employee mental health comprehension in the technology industry.

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“…The use of ML algorithms and various AI-based models is rapidly advancing in various healthcare sectors and research institutions, 42 particularly in cancer research, with enormous practical applications. 43 ML techniques are increasingly being utilized in medical diagnostics, primarily because classification and identification systems are more efficient in assisting medical professionals in disease diagnosis. 3 Thorough evaluation of AI systems paves the way for clinical studies aimed at enhancing the accuracy and effectiveness of BC diagnosis.…”

Section: The Approach Usedmentioning

confidence: 99%

Breast cancer diagnosis and management guided by data augmentation, utilizing an integrated framework of SHAP and random augmentation

Ejiyi,

Qin,

Monday

et al. 2023

BioFactors

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Recent research indicates that early detection of breast cancer (BC) is critical in achieving favorable treatment outcomes and reducing the mortality rate associated with it. With the difficulty in obtaining a balanced dataset that is primarily sourced for the diagnosis of the disease, many researchers have relied on data augmentation techniques, thereby having varying datasets with varying quality and results. The dataset we focused on in this study is crafted from SHapley Additive exPlanations (SHAP)‐augmentation and random augmentation (RA) approaches to dealing with imbalanced data. This was carried out on the Wisconsin BC dataset and the effectiveness of this approach to the diagnosis of BC was checked using six machine‐learning algorithms. RA synthetically generated some parts of the dataset while SHAP helped in assessing the quality of the attributes, which were selected and used for the training of the models. The result from our analysis shows that the performance of the models used generally increased to more than 3% for most of the models using the dataset obtained by the integration of SHAP and RA. Additionally, after diagnosis, it is important to focus on providing quality care to ensure the best possible outcomes for patients. The need for proper management of the disease state is crucial so as to reduce the recurrence of the disease and other associated complications. Thus the interpretability provided by SHAP enlightens the management strategies in this study focusing on the quality of care given to the patient and how timely the care is.

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Comparing the performance of machine learning algorithms using estimated accuracy

Cited by 40 publications

References 6 publications

Comparison of Machine Learning Algorithms for Predicting Patients with Suspected COVID-19

Comparison of Machine Learning Algorithms for Predicting Patients with Suspected COVID-19

Prediction of Mental Health Support of Employee Perceiving by Using Machine Learning Methods

Breast cancer diagnosis and management guided by data augmentation, utilizing an integrated framework of SHAP and random augmentation

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