Classification of Dengue Serotypes Using Protein Sequence Based on Rule Extraction from Neural Network

Pandiyarajan, Pandiselvam; Kathirvalavakumar, T.

doi:10.1007/978-3-030-05918-7_12

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…It may be concluded that more work must still be carried out on DF. Overall, very few researchers worked on correctly identifying infected patients and categorizing them based on severity level, such as DF, DHF, or DSS [11][12][13]. Even if they used a clinical dataset, i.e., patient's clinical test results as primary input, they struggled to evaluate from which stage the patient was suffering or the prognosis of one stage to a more deadly one, and the main focus is only on identifying infected people among the population [14][15][16].…”

Section: Dengue Fever Infectionmentioning

confidence: 99%

Predicting Infection Positivity, Risk Estimation, and Disease Prognosis in Dengue Infected Patients by ML Expert System

et al. 2022

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Dengue fever has earned the title of a rapidly growing global epidemic since the disease-causing mosquito has adapted to colder countries, breaking the notion of dengue being a tropical/subtropical disease only. This infectious time bomb demands timely and proper treatment as it affects vital body functions, often resulting in multiple organ failures once thrombocytopenia and internal bleeding manifest in the patients, adding to morbidity and mortality. In this paper, a tool is used for data collection and analysis for predicting dengue infection presence and estimating risk levels to identify which group of dengue infections the patient suffers from, using a machine-learning-based tertiary classification technique. Based on symptomatic and clinical investigations, the system performs real-time diagnosis. It uses warning indicators to alert the patient of possible internal hemorrhage, warning them to seek medical assistance in case of this disease-related emergency. The proposed model predicts infection levels in a patient based on the classification provided by the World Health Organization, i.e., dengue fever, dengue hemorrhagic fever, and dengue shock syndrome, acquiring considerably high accuracy of over 90% along with high sensitivity and specificity values. The experimental evaluation of the proposed model acknowledges performance efficiency and utilization through statistical approaches.

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Section: Dengue Fever Infectionmentioning

confidence: 99%

Predicting Infection Positivity, Risk Estimation, and Disease Prognosis in Dengue Infected Patients by ML Expert System

et al. 2022

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“…Dengue is often labeled as the most ignored tropical disease irrespective of the medical advancements achieved; to date, no vaccine treatment is available [8]. For this reason, researchers have comprehensively explored mechanisms to predict and diagnose this disease with a special focus on designing intelligent medical expert systems that could accurately and timely predict Dengue which could save many lives in an area having minimal or no medical facility available [9,10]. This paper not only summarizes the various works done in the literature for devising expert systems but also deploys various machine learning algorithms to effectively predict dengue-infected persons.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Machine Learning Classifiers on Forecasting Dengue Fever Infection

Kaur¹,

Sharma²

2023

Advances in Transdisciplinary Engineering

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Each year Dengue infection causes havoc almost across the entire globe raising the death toll and thus becoming a global burden. With the absence of Global approved vaccine and its scope not limiting itself to tropical regions anymore, it has become a rapidly growing epidemic. Researchers have comprehensively explored mechanisms to predict and diagnose infectious diseases and machine learning has revolutionized the medical field by reducing the computing time in analyzing complex data and finding hidden patterns for accurate predictions. In this study, a comparative machine learning-based analysis is achieved using python based classification to predict dengue fever infection in a person. This paper helps interested researchers choose the most efficient classification technique among the selected machine learning classifiers by not only focusing on accuracy achieved but on overall classification metrics analysis to develop a better Dengue predictive model.

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Novel Predictive Machine Learning Approach for Identification of Microbial Niche and Microbial Communities from Omics Dataset of Kaveri River, Tamil-Nadu, India

Pandiyarajan,

Maheswaran,

Raj

et al. 2024

Lecture Notes in Networks and Systems

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Classification of Dengue Serotypes Using Protein Sequence Based on Rule Extraction from Neural Network

Cited by 3 publications

References 8 publications

Predicting Infection Positivity, Risk Estimation, and Disease Prognosis in Dengue Infected Patients by ML Expert System

Predicting Infection Positivity, Risk Estimation, and Disease Prognosis in Dengue Infected Patients by ML Expert System

Comparative Analysis of Machine Learning Classifiers on Forecasting Dengue Fever Infection

Novel Predictive Machine Learning Approach for Identification of Microbial Niche and Microbial Communities from Omics Dataset of Kaveri River, Tamil-Nadu, India

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