ArticleBiomedical optics express2016
Analysis of dengue infection based on Raman spectroscopy and support vector machine (SVM).
Article in Biomedical optics express, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The application of spectroscopy techniques for diagnosis of malaria parasites and arboviruses and surveillance of mosquito vectors: A systematic review and critical appraisal of evidence.PLoS neglected tropical diseases · 2021Pooled it
- Tree-Based Machine Learning for Diagnostic Classification of Dengue Fever Using Routine Hematological Parameters: A Secondary Analysis of a Publicly Available Dataset.Diagnostics (Basel, Switzerland) · 2026Article
- Quantum vibrational spectroscopy with classical trajectories.Chemical science · 2026Review
- A novel hybrid model for emotion detection in text through sequential and transformer-based approaches: LSTM enhanced RoBERTa (LER).Scientific reports · 2026Article
- Chemometric Methods Applied to Infrared and Raman Spectroscopy for Arboviruses Diagnosis: A Systematic Review With Meta-Analysis.Analytical science advances · 2025Review
- FTIR-based molecular fingerprinting for the rapid classification of dengue and chikungunya from human sera using machine learning: an observational study.The Lancet regional health. Southeast Asia · 2025Article
- Indirect Detection of Swine Influenza Activity in Porcine Blood Using Raman Spectroscopy and Machine Learning.Journal of biophotonics · 2025Article
- Direct feature identification from Raman spectra and precise data-driven classification of phytopathogens at single conidium-species level.Computational and structural biotechnology journal · 2025Article
- Detection and Identification of Pesticides in Fruits Coupling to an Au-Au Nanorod Array SERS Substrate and RF-1D-CNN Model Analysis.Nanomaterials (Basel, Switzerland) · 2024Article
- Using Machine Learning and Silver Nanoparticle-Based Surface-Enhanced Raman Spectroscopy for Classification of Cardiovascular Disease Biomarkers.ACS applied nano materials · 2023Article
- Raman spectroscopy combined with a support vector machine algorithm as a diagnostic technique for primary Sjögren's syndrome.Scientific reports · 2023Article
- Deep learning models for hepatitis E incidence prediction leveraging meteorological factors.PloS one · 2023Article
- A Machine Learning Framework for Detecting COVID-19 Infection Using Surface-Enhanced Raman Scattering.Biosensors · 2022Article
- Diagnosis of dengue virus infection using spectroscopic images and deep learning.PeerJ. Computer science · 2022Article
- Software Defect Prediction for Healthcare Big Data: An Empirical Evaluation of Machine Learning Techniques.Journal of healthcare engineering · 2021Article
- Extracellular Vesicle Identification Using Label-Free Surface-Enhanced Raman Spectroscopy: Detection and Signal Analysis Strategies.Molecules (Basel, Switzerland) · 2020Review
- Rapid screening of hepatitis B using Raman spectroscopy and long short-term memory neural network.Lasers in medical science · 2020Article
- Severity Index for Suspected Arbovirus (SISA): Machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection.PLoS neglected tropical diseases · 2020Article
- Performance Assessment of Classification Algorithms on Early Detection of Liver Syndrome.Journal of healthcare engineering · 2020Article
- Prediction of hepatitis E using machine learning models.PloS one · 2020Article
Corrections and comments
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6 authors.
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Abstract
The current study presents the use of Raman spectroscopy combined with support vector machine (SVM) for the classification of dengue suspected human blood sera. Raman spectra for 84 clinically dengue suspected patients acquired from Holy Family Hospital, Rawalpindi, Pakistan, have been used in this study.The spectral differences between dengue positive and normal sera have been exploited by using effective machine learning techniques. In this regard, SVM models built on the basis of three different kernel functions including Gaussian radial basis function (RBF), polynomial function and linear functionhave been employed to classify the human blood sera based on features obtained from Raman Spectra.The classification model have been evaluated with the 10-fold cross validation method. In the present study, the best performance has been achieved for the polynomial kernel of order 1. A diagnostic accuracy of about 85% with the precision of 90%, sensitivity of 73% and specificity of 93% has been achieved under these conditions.
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