Machine learning approaches in the diagnosis of infectious diseases: a review

Mishra, Smriti; Kumar, Ranjan; Tiwari, Sanjay; Ranjan, Priya

doi:10.11591/eei.v11i6.4225

Cited by 15 publications

(14 citation statements)

References 40 publications

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“…Machine learning algorithms detect data patterns to recognise infectious diseases [19]. Further, those algorithms can be trained using an extensive dataset of true and false information to develop an accurate and 1035 reliable hoax detection system.…”

Section: Machine Learning Algorithms For Hoax Classificationmentioning

confidence: 99%

Fast Naïve Bayes classifiers for COVID-19 news in social networks

Cahyono,

Mahadewa,

Wijayanto

et al. 2024

IJEECS

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The growth of fake news has emerged as a substantial societal concern, particularly in the context of the COVID-19 pandemic. Fake news can lead to unwarranted panic, misinformed decisions, and a general state of confusion among the public. Existing methods to detect and filter out fake news have accuracy, speed, and data distribution limitations. This study explores a fast and reliable approach based on Naïve Bayes algorithms for fake news detection on COVID-19 news in social networks. The study used a dataset of 10,700 tweets and applied text pre-processing, term-weighting, document frequency thresholding (DFT), and synthetic minority oversampling techniques (SMOTE) to prepare the data for classification. The study assessed the performance and runtime of four models: gradient boosting (GDBT), decision tree (DT), multinomial Naïve Bayes (MNB), and complement Naïve Bayes (CNB). The testing results showed that the CNB model reaches the highest accuracy, precision, recall, and F1-score of approximately 92% each, with the shortest runtime of 0.55 seconds. This study highlights the potential of the CNB model as an effective tool for detecting online fake news about COVID-19, given its superior performance and rapid processing time.

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Section: Machine Learning Algorithms For Hoax Classificationmentioning

confidence: 99%

Fast Naïve Bayes classifiers for COVID-19 news in social networks

Cahyono,

Mahadewa,

Wijayanto

et al. 2024

IJEECS

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“…In the next stage, SVM will find the best maximum marginal hyperplane (MMH) in dividing the dataset into classes [32]. In SVM, there are support vectors, the data (points) closest to the hyperplane [33]. This point will define a hyperplane that separates sets of objects with different class memberships by calculating the margins [34].…”

Section: Support Vector Machinementioning

confidence: 99%

Classification of six banana ripeness levels based on statistical features on machine learning approach

Kosasih,

Sudaryanto,

Fahrurozi

2023

IJAAS

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<span>Banana plants are often cultivated because they have many benefits. In producing, we need to maintain the quality of bananas by looking at banana ripeness levels before being distributed to markets. The level of banana ripeness is related to marketing reach. If the marketing reach is far, bananas should be harvested when the ripeness level of bananas is still relatively low. A system that can classify the degree of ripeness of bananas can help overcome this problem. In this study, our dataset includes 6 ripeness levels of bananas, more than in previous related studies. Furthermore, we use the statistical features extraction method to find the parameters that affect the level of banana ripeness, considering the texture and color of the banana peel which determines the level of ripeness visually. The extraction used is features extraction based on a histogram, then we employ four features, i.e., mean, skewness, energy descriptor, and smoothness, generated from the image dataset. In the next stage, we perform classification based on the features that have been obtained. In this study, we use Naive Bayes classifier and support vector machine (SVM) algorithms. Based on the result of this research, the best performance is the Naive Bayes classifier, with an accuracy is 86.67%, a weighted average precision of 83.55%, and a weighted average recall of 86.67%.</span>

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“…Because DL models' hidden layers automatically extract features, they do not require the calculation of hand-crafted features, through data analysis. Large datasets are needed for DL models [11] to train neural networks and increase model accuracy [16]. Nonetheless, comparatively modest datasets are accessible for medical applications such as malaria detection.…”

Section: Introductionmentioning

confidence: 99%

Malaria cell identification using improved machine learning and modified deep learning architecture

S.,

H. D.

2024

IJEECS

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Malaria continues to be a serious problem for public health because of its occurrence in tropical and subtropical areas with inadequate healthcare systems and few resources. For prompt intervention and treatment of malaria, effective and precise diagnosis is essential. Professional pathologists examine blood smear films by hand to get a microscopic diagnosis and another way they will do a rapid antigen malaria test which produces the result of 50% accuracy. Convolutional neural network (CNN) is a type of deep learning (DL) model that has been effectively used for a variety of image recognition applications. Our suggested approach uses, improved machine learning (IML) methods like support vector machine (SVM)+principal component analysis (PCA) fit, SVM+t-distributed stochastic neighbor embedding (t-SNE) fit, and CNN architecture with an accuracy of 86.23%, 88.27%, and 97.16% accuracy respectively, to combine feature extraction, data augmentation, and modify the layers by including the SVM algorithm in the final layer of the CNN architecture. The proposed method will significantly reduce pathologists' burden by automating the identification of malaria and improving diagnosis accuracy in resourceconstrained contexts

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Machine learning approaches in the diagnosis of infectious diseases: a review

Cited by 15 publications

References 40 publications

Fast Naïve Bayes classifiers for COVID-19 news in social networks

Fast Naïve Bayes classifiers for COVID-19 news in social networks

Classification of six banana ripeness levels based on statistical features on machine learning approach

Malaria cell identification using improved machine learning and modified deep learning architecture

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