Supervised Machine Learning Methods for Seasonal Influenza Diagnosis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Set
2.2. Data Preprocessing
2.3. Machine Learning Algorithms
2.4. Validation
3. Results
4. Discussion
4.1. Limitations of Work
4.2. Future Work
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Attributes | All Patients n = 15,480 n (%) | Positive n = 4212 n (%) | Negative n = 11,268 n (%) | p-Value |
---|---|---|---|---|
Demographic information | ||||
Sex—Feminine | 7770 (50.2) | 2162 (51.3) | 5608 (49.8) | 0.087 |
Sex—Masculine | 7710 (49.8) | 2050 (48.7) | 5660 (50.2) | |
Hospitalized | 10516 (67.9) | 2449 (58.1) | 8067 (71.6) | <0.001 |
Contact influenza-patients | 2012 (13.0) | 715 (17.0) | 1297 (11.5) | <0.001 |
Vaccinated for influenza | 2096 (13.5) | 534 (12.7) | 1562 (13.9) | 0.059 |
Age 7–19 years | 6417 (41.5) | 1511 (35.9) | 4906 (43.5) | <0.001 |
Age 20–39 years | 3111 (20.1) | 967 (23.0) | 2144 (19.0) | |
Age 40–59 years | 3283 (21.2) | 1056 (25.0) | 2227 (19.8) | |
Age ≥ 60 years | 2669 (17.2) | 678 (16.1) | 1991 (17.7) | |
Symptoms | ||||
Fever | 13,112 (84.7) | 3853 (84.2) | 9259 (82.2) | <0.001 |
Cough | 13,953 (90.1) | 3918 (85.7) | 10,035 (89.1) | <0.001 |
Chest pain | 3750 (24.2) | 1160 (25.4) | 2590 (23.0) | <0.001 |
Dyspnea | 8642 (55.8) | 2079 (45.5) | 6563 (58.2) | <0.001 |
Irritability | 4688 (30.3) | 1159 (25.3) | 3529 (31.3) | <0.001 |
Diarrhea | 1833 (11.8) | 492 (10.8) | 1341 (11.9) | 0.727 |
Shaking chills | 5738 (37.1) | 2003 (43.8) | 3735 (33.1) | <0.001 |
Headache | 8692 (56.1) | 2896 (63.3) | 5796 (51.4) | <0.001 |
Myalgia | 6255 (40.4) | 2279 (49.8) | 3976 (35.3) | <0.001 |
Arthralgia | 5539 (35.8) | 2014 (44.0) | 3525 (31.3) | <0.001 |
Malaise | 9826 (63.5) | 2947 (64.4) | 6879 (61.0) | <0.001 |
Rhinorrhea | 9277 (59.9) | 2817 (61.6) | 6460 (57.3) | <0.001 |
Polypnea | 4602 (29.7) | 1073 (23.5) | 3529 (31.3) | <0.001 |
Vomiting | 1958 (12.6) | 606 (13.2) | 1352 (12.0) | <0.001 |
Abdominal pain | 2114 (13.7) | 683 (14.9) | 1431 (12.7) | <0.001 |
Sore throat | 5321 (34.4) | 1850 (40.4) | 3471 (30.8) | <0.001 |
Conjunctivitis | 3074 (19.9) | 1104 (24.1) | 1970 (17.5) | <0.001 |
Cyanosis | 1703 (11.0) | 395 (8.6) | 1308 (11.6) | <0.001 |
Algorithm | AUC | Acc | Rec | Prec | Spec | F1 |
---|---|---|---|---|---|---|
Random Forest | 0.94 | 0.86 | 0.91 | 0.82 | 0.88 | 0.86 |
Bagging | 0.93 | 0.85 | 0.90 | 0.82 | 0.87 | 0.85 |
Decision Tree | 0.85 | 0.70 | 0.71 | 0.73 | 0.73 | 0.72 |
Kneighbors (7) | 0.73 | 0.63 | 0.67 | 0.63 | 0.60 | 0.63 |
Gradient Boosting | 0.69 | 0.62 | 0.69 | 0.61 | 0.56 | 0.62 |
SVM rbf | 0.67 | 0.62 | 0.65 | 0.61 | 0.59 | 0.62 |
Quadratic Discriminant | 0.66 | 0.62 | 0.70 | 0.60 | 0.54 | 0.62 |
Ada Boost | 0.66 | 0.62 | 0.62 | 0.61 | 0.61 | 0.62 |
Linear Discriminant * | 0.65 | 0.61 | 0.62 | 0.61 | 0.61 | 0.61 |
Linear SVM * | 0.65 | 0.61 | 0.62 | 0.61 | 0.61 | 0.61 |
Logistic Regression | 0.65 | 0.61 | 0.62 | 0.61 | 0.61 | 0.61 |
BernoulliNB | 0.65 | 0.61 | 0.59 | 0.61 | 0.62 | 0.61 |
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Marquez, E.; Barrón-Palma, E.V.; Rodríguez, K.; Savage, J.; Sanchez-Sandoval, A.L. Supervised Machine Learning Methods for Seasonal Influenza Diagnosis. Diagnostics 2023, 13, 3352. https://doi.org/10.3390/diagnostics13213352
Marquez E, Barrón-Palma EV, Rodríguez K, Savage J, Sanchez-Sandoval AL. Supervised Machine Learning Methods for Seasonal Influenza Diagnosis. Diagnostics. 2023; 13(21):3352. https://doi.org/10.3390/diagnostics13213352
Chicago/Turabian StyleMarquez, Edna, Eira Valeria Barrón-Palma, Katya Rodríguez, Jesus Savage, and Ana Laura Sanchez-Sandoval. 2023. "Supervised Machine Learning Methods for Seasonal Influenza Diagnosis" Diagnostics 13, no. 21: 3352. https://doi.org/10.3390/diagnostics13213352