Machine learning and deep learning techniques to support clinical diagnosis of arboviral diseases: A systematic review
Background Neglected tropical diseases (NTDs) primarily affect the poorest populations,
often living in remote, rural areas, urban slums or conflict zones. Arboviruses are a …
often living in remote, rural areas, urban slums or conflict zones. Arboviruses are a …
A comparative analysis of converters of tabular data into image for the classification of Arboviruses using Convolutional Neural Networks
Tabular data is commonly used in business and literature and can be analyzed using tree-
based Machine Learning (ML) algorithms to extract meaningful information. Deep Learning …
based Machine Learning (ML) algorithms to extract meaningful information. Deep Learning …
A comparative study of machine learning techniques for multi-class classification of arboviral diseases
Among the neglected tropical diseases (NTDs), arboviral diseases present a significant
number of cases worldwide. Their correct classification is a complex process due to the …
number of cases worldwide. Their correct classification is a complex process due to the …
[PDF][PDF] A Multi-Class Classification of Dengue Infection Cases with Feature Selection in Imbalanced Clinical Diagnosis Data.
Dengue infection is a dangerous infectious disease that threatens human health at every
age and can be deadly. The imbalance of the dengue infection disease dataset will interfere …
age and can be deadly. The imbalance of the dengue infection disease dataset will interfere …
Performance analysis of the imbalanced data method on increasing the classification accuracy of the machine learning hybrid method
AA Rahman, SS Prasetiyowati… - JIPI (Jurnal …, 2023 - jurnal.stkippgritulungagung.ac.id
This study analyzes the performance of hybrid methods in improving accuracy on
imbalanced data using Dengue Hemorrhagic Fever Case Data from 2017 to 2021 in …
imbalanced data using Dengue Hemorrhagic Fever Case Data from 2017 to 2021 in …
Performance Analysis of the Ada-Boost Algorithm For Classification of Hypertension Risk With Clinical Imbalanced Dataset
High blood pressure, another name for hypertension, is a condition in which the pressure
inside the blood arteries keeps rising. One of the most exciting fields of study is data mining …
inside the blood arteries keeps rising. One of the most exciting fields of study is data mining …
[PDF][PDF] Enhancing Prediction Accuracy in an Imbalanced Dataset of Dengue Infection Cases Using a Two-layer Ensemble Outlier Detection and Feature Selection …
Real-world datasets frequently compromise considerably on noise, resulting in the
emergence of outlier data. Detecting and removing outliers in large and imbalanced …
emergence of outlier data. Detecting and removing outliers in large and imbalanced …
[PDF][PDF] Multi-Label Classification for Doctor's Behavioral Pattern Matching During Online Medical Interview using Machine Learning.
In recent years, many studies on medical texts have attracted the attention of researchers.
Medical text studies have few multi-label data targets because it is challenging to …
Medical text studies have few multi-label data targets because it is challenging to …
A hybrid machine learning-powered intelligent system for enhancing dengue patient safety and care
CS Kumar, KRL Gupta, GM Patel… - Multidisciplinary Science …, 2024 - malque.pub
Dengue fever is a significant global health concern, with millions of cases reported each
year, leading to considerable morbidity and mortality. Early diagnosis, patient monitoring …
year, leading to considerable morbidity and mortality. Early diagnosis, patient monitoring …
A Comparative Study on Machine Learning based Prediction Models for Public Participation Rate in an Election Voting
Prediction of public participation in elections is one measure of election success. Voter
participation is at the polling station level and involves four data sources: voters, polling …
participation is at the polling station level and involves four data sources: voters, polling …