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Septicemic Melioidosis Detection Using Support Vector Machine with Five Immune Cell Types
Ke Xu1, Fang Lian2, Yunfan Quan1
1Key Laboratory of Tropical Translational Medicine of Ministry of Education and School of Tropical Medicine and Laboratory Medicine, Hainan Medical University, Haikou, Hainan, China.
Abstract:
Melioidosis, caused by Burkholderia pseudomallei (B. pseudomallei), predominantly occurs in the tropical regions. Of various types of melioidosis, septicemic melioidosis is the most lethal one with a mortality rate of 40%. Early detection of the disease is paramount for the better chances of cure. In this study, we developed a novel approach for septicemic melioidosis detection, using a machine learning technique-support vector machine (SVM). Several SVM models were built, and 19 features characterized by the corresponding immune cell types were generated by Cell type Identification Estimating Relative Subsets Of RNA Transcripts (CIBERSORT). Using these features, we trained a binomial SVM model on the training set and evaluated it on the independent testing set. Our findings indicated that this model performed well with means of sensitivity and specificity up to 0.962 and 0.979, respectively. Meanwhile, the receiver operating characteristic (ROC) curve analysis gave area under curves (AUCs) ranging from 0.952 to 1.000. Furthermore, we found that a concise SVM model, built upon a combination of CD8+ T cells, resting CD4+ memory T cells, monocytes, M2 macrophages, and activated mast cells, worked perfectly on the detection of septicemic melioidosis. Our data showed that its mean of sensitivity was up to 0.976 while that of specificity up to 0.993. In addition, the ROC curve analysis gave AUC close to 1.000. Taken together, this SVM model is a robust classification tool and may serve as a complementary diagnostic technique to septicemic melioidosis.
Insights
A novel machine learning approach using support vector machine (SVM) effectively detects septicemic melioidosis. This method, analyzing immune cell subsets, offers high accuracy for early diagnosis of this lethal infection caused by Burkholderia pseudomallei.
Area of Science:
- Infectious Diseases
- Computational Biology
- Immunology
Background:
- Melioidosis, caused by Burkholderia pseudomallei, is prevalent in tropical regions.
- Septicemic melioidosis is the most lethal form, with a 40% mortality rate.
- Early detection is critical for improving patient outcomes.
Purpose of the Study:
- To develop a novel machine learning-based approach for detecting septicemic melioidosis.
- To utilize immune cell profiling for disease identification.
- To evaluate the performance of support vector machine (SVM) models in diagnosing septicemic melioidosis.
Main Methods:
- Development of multiple SVM models for septicemic melioidosis detection.
- Generation of 19 immune cell-related features using Cell type Identification Estimating Relative Subsets Of RNA Transcripts (CIBERSORT).
- Training and independent evaluation of a binomial SVM model using these features.
Main Results:
- The SVM model demonstrated high performance with sensitivity up to 0.962 and specificity up to 0.979.
- Receiver operating characteristic (ROC) curve analysis showed Area Under Curves (AUCs) ranging from 0.952 to 1.000.
- A concise SVM model using specific immune cell types (CD8+ T cells, resting CD4+ memory T cells, monocytes, M2 macrophages, activated mast cells) achieved sensitivity of 0.976 and specificity of 0.993.
Conclusions:
- The developed SVM model is a robust classification tool for septicemic melioidosis detection.
- This machine learning approach can serve as a complementary diagnostic technique.
- Accurate identification of septicemic melioidosis through immune cell analysis can aid in timely treatment and improve prognosis.
