Comparative Analysis of Ml Algorithms &amp; Stream Lit Web Application

Selvin, Paul R.; Maheshwari, Arushi; Johri, Prashant

doi:10.1109/icac3n53548.2021.9725496

Cited by 21 publications

(12 citation statements)

References 11 publications

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“…Esta seção detalha o desenvolvimento da aplicação web para apresentação e interpretação dos resultados obtidos usando o método proposto descrito na Seção 6.1. Para isso, toda a aplicação foi desenvolvida na linguagem de programação Python [Millman and Aivazis 2011], usando as bibliotecas OpenCV [Bradski 2000], Mahotas [Coelho 2012], XGBoost [Chen and Guestrin 2016], Imblearn [Mishra 2017], LIME [Ribeiro et al 2016] e Streamlit [Shukla et al 2021].…”

Section: Resultsunclassified

Desenvolvimento de uma aplicação web para o diagnóstico da COVID-19 usando conceitos, técnicas e ferramentas de Inteligência Artificial

Silva¹,

Filho²,

Magno³

et al. 2022

Minicursos Da ERCEMAPI 2022

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The pandemic caused by COVID-19 has drastically affected the health and well-being of the world's population. However, early detection is essential not only for the treatment and cure of patients, but also for public health, ensuring the isolation of patients. So far, several screening methods have been introduced for the diagnosis of COVID-19, including the analysis of medical images. Recent studies prove that Artificial Intelligence can be useful for a fast and accurate diagnosis. Therefore, the mini-course aims to develop a web application for the diagnosis of COVID-19, through chest radiography images, using concepts, techniques and tools of Artificial Intelligence and Digital Image Processing, being able to provide a second opinion to specialists. ResumoA pandemia causada pela COVID-19 afetou drasticamente a saúde e o bem-estar da população mundial. No entanto, a detecção precoce é essencial não só para o tratamento e cura dos pacientes, mas também para a saúde pública, garantindo o isolamento dos pacientes. Até o momento, vários métodos de triagem foram introduzidos para o diagnóstico da COVID-19, incluindo a análise de imagens médicas. Estudos recentes comprovam que a Inteligência Artificial pode ser útil para um diagnóstico rápido e preciso. Portanto, o minicurso tem como objetivo desenvolver uma aplicação web para o diagnóstico da COVID-19, por meio de imagens de radiografia do tórax, usando conceitos, técnicas e ferramentas de Inteligência Artificial e Processamento de Imagens Digitais, podendo fornecer uma segunda opinião aos especialistas.

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Section: Resultsunclassified

Desenvolvimento de uma aplicação web para o diagnóstico da COVID-19 usando conceitos, técnicas e ferramentas de Inteligência Artificial

Silva¹,

Filho²,

Magno³

et al. 2022

Minicursos Da ERCEMAPI 2022

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“…Thus, some data follow one branch while the rest follow another. Therefore, each subsequent node receives fewer samples than the previous node ( 17 ) . The decision tree is among the most elementary machine learning algorithms, facilitating understanding for healthcare professionals as the interpretability and explainability of the model are evident.…”

Section: Discussionmentioning

confidence: 99%

Web App for prediction of hospitalisation in Intensive Care Unit by covid-19

Fabrizzio,

Erdmann,

Oliveira

2023

Rev. Bras. Enferm.

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Objective: To develop a Web App from a predictive model to estimate the risk of Intensive Care Unit (ICU) admission for patients with covid-19. Methods: An applied technological production research was carried out with the development of Streamlit using Python, considering the decision tree model that presented the best performance (AUC 0.668). Results: Based on the variables associated with Precision Nursing, Streamlit stratifies patients admitted to clinical units who are most likely to be admitted to the Intensive Care Unit, serving as a decision-making support tool for healthcare professionals. Final considerations: The performance of the model may have been influenced by the start of vaccination during the data collection period, however, the Web App via Streamlit proved to be a feasible tool for presenting research results, due to the ease of understanding by nurses and its potential for supporting clinical decision-making.

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“…Due to limited resources, the prototype for the proposed model was developed in a Kaggle environment, and then the (.sav) le of the ensemble model was exported and stored on a local disc. Besides, to develop a web-based interface, Streamlit was utilised, and code was written in a Python 3.9 environment and run on the Anaconda Navigator command terminal [43]. And the code to run the model was written on the command terminal, and once it is run, it will redirect to the web server on the same machine via the default browser.…”

Section: Predictive Model Developmentmentioning

confidence: 99%

“…Streamlit is an open-source app framework in the Python language. It helps us create web apps for data science and machine learning in a short time[43]. It is compatible with major Python libraries such as scikit-learn, Keras, PyTorch, SymPy (latex), NumPy, Pandas, Matplotlib, etc3 Results and DiscussionSetup and Experimentation of ModelsA brief description of the hardware and software environments utilized to carry out the experiments is given below.…”

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confidence: 99%

Multi-Category Prediction of Students’ Academic Performance Using Machine Learning: For Students Joining Higher Educational Institutions in Ethiopia

Ahmed,

Tolera

2023

Preprint

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Following the deployment of the Learning Management System (LMS) platform in higher educational institutions in Ethiopia, a massive amount of potentially helpful but as-yet untapped educational data has been generated. Despite the fact that the data is powerful enough to contribute to reducing student dropout rates through the application of modern educational data mining techniques such as machine learning, it has not been successfully employed to tackle student academic performance problems in higher education institutions(HEIs). As a result, a machine learning model was proposed based on data from three semesters of undergraduate students at Bule Hora University. To predict students' academic achievement, five machine learning methods (SVM, Random Forest, KNN, Gradient Boosting, and Decision Tree) were used. The Decision Tree model outperformed other models with a promising result of 97.3% on test accuracy and was selected as a proposed model. Moreover, our findings suggest that academic factors (entrance result, study time, attendance, and Internet access) and socio-demographic factors (age, gender, father job, mother job, family size, and address) had a greater impact on students' academic success. However, the academic performance of students was less affected by additional features like extra classes, another job, and guidance and counselling. Moreover, we are working on improving the accuracy of the proposed model. In this study, the student entrance result is the only variable used from the pre-university data. However, a CGPA result is not sufficient to qualify a student. Therefore, in the future, the exit exam results of the students will be incorporated.

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Comparative Analysis of Ml Algorithms & Stream Lit Web Application

Cited by 21 publications

References 11 publications

Desenvolvimento de uma aplicação web para o diagnóstico da COVID-19 usando conceitos, técnicas e ferramentas de Inteligência Artificial

Desenvolvimento de uma aplicação web para o diagnóstico da COVID-19 usando conceitos, técnicas e ferramentas de Inteligência Artificial

Web App for prediction of hospitalisation in Intensive Care Unit by covid-19

Multi-Category Prediction of Students’ Academic Performance Using Machine Learning: For Students Joining Higher Educational Institutions in Ethiopia

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