Malaria Outbreak Detection with Machine Learning Methods

Comert, Gurcan; Begashaw, Negash; Turhan-Comert, Ayse

doi:10.1101/2020.07.21.214213

Cited by 8 publications

(6 citation statements)

References 5 publications

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“…ML has also been successfully applied in epidemiological studies of malaria [103,104]. The outbreak of malaria using six observed variables; a dataset of thirty-eight compounds collected from malaria samples of Maharashtra State with eight descriptors was used [105].…”

Section: As a Regression Toolmentioning

confidence: 99%

Systematic review on the application of machine learning to quantitative structure–activity relationship modeling against Plasmodium falciparum

et al. 2022

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Malaria accounts for over two million deaths globally. To flatten this curve, there is a need to develop new and high potent drugs against Plasmodium falciparum . Some major challenges include the dearth of suitable animal models for anti- P. falciparum assays, resistance to first-line drugs, lack of vaccines and the complex life cycle of Plasmodium . Gladly, newer approaches to antimalarial drug discovery have emerged due to the release of large datasets by pharmaceutical companies. This review provides insights into these new approaches to drug discovery covering different machine learning tools, which enhance the development of new compounds. It provides a systematic review on the use and prospects of machine learning in predicting, classifying and clustering IC 50 values of bioactive compounds against P. falciparum . The authors identified many machine learning tools yet to be applied for this purpose. However, Random Forest and Support Vector Machines have been extensively applied though on a limited dataset of compounds.

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Section: As a Regression Toolmentioning

confidence: 99%

Systematic review on the application of machine learning to quantitative structure–activity relationship modeling against Plasmodium falciparum

et al. 2022

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“…They were able to detect the samples based on the sample data used. Malaria outbreak in the testing dataset without any false positive or false negative errors [30].…”

Section: Literature Reviewmentioning

confidence: 99%

Optimized hybrid investigative based dimensionality reduction methods for malaria vector using KNN classifier

Arowolo

Adebiyi

et al. 2021

J Big Data

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RNA-Seq data are utilized for biological applications and decision making for the classification of genes. A lot of works in recent time are focused on reducing the dimension of RNA-Seq data. Dimensionality reduction approaches have been proposed in the transformation of these data. In this study, a novel optimized hybrid investigative approach is proposed. It combines an optimized genetic algorithm with Principal Component Analysis and Independent Component Analysis (GA-O-PCA and GAO-ICA), which are used to identify an optimum subset and latent correlated features, respectively. The classifier uses KNN on the reduced mosquito Anopheles gambiae dataset, to enhance the accuracy and scalability in the gene expression analysis. The proposed algorithm is used to fetch relevant features based on the high-dimensional input feature space. A fast algorithm for feature ranking is used to select relevant features. The performances of the model are evaluated and validated using the classification accuracy to compare existing approaches in the literature. The achieved experimental results prove to be promising for selecting relevant genes and classifying pertinent gene expression data analysis by indicating that the approach is capable of adding to prevailing machine learning methods.

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“…It also aids in identifying and extracting key information and patterns from signals, helping reveal underlying physiological or pathological characteristics embedded in the signal. Feature extraction is primarily achieved by extracting features from the time domain [16], frequency domain [17], time-frequency domain [18], decomposition domain [19], and deep features using filters with different functions [20].…”

Section: Introductionmentioning

confidence: 99%

Establishment and Application of Recursive Digital Processing Algorithm with Difference Analysis-Based Parameter Setting and Difference Feedback

Xia,

Wang,

Huang

et al. 2024

Preprint

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Signal preprocessing and feature extraction are two important and necessary steps of data analysis. In signal analysis, typically these two steps are implemented through multiple algorithms. Moreover, the significant parameters in most algorithms are set empirically. We have developed an algorithm that can preserve and highlight characteristic waveforms while attenuating non-feature waveforms. The parameters in the algorithm are set through difference analysis. We have conducted experiments on both clinical and open-source ECG data, obtaining effective test results.

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Malaria Outbreak Detection with Machine Learning Methods

Cited by 8 publications

References 5 publications

Systematic review on the application of machine learning to quantitative structure–activity relationship modeling against Plasmodium falciparum

Systematic review on the application of machine learning to quantitative structure–activity relationship modeling against Plasmodium falciparum

Optimized hybrid investigative based dimensionality reduction methods for malaria vector using KNN classifier

Establishment and Application of Recursive Digital Processing Algorithm with Difference Analysis-Based Parameter Setting and Difference Feedback

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