Name
Title | ||
|---|---|---|
A Machine Learning Model to Identify Early Stage Symptoms of SARS-Cov-2 Infected Patients |
Abstract | ||
|---|---|---|
•Machine learning was used to develop models to predict COVID-19 positive patient.•Features were extracted from patient data using string matching algorithms.•Constructed a novel dataset from unstructured hospitalized patient information.•Used descriptive statistical analysis for frequency calculation of patient symptoms.•Identified significant symptoms of COVID-19 patients using five different ML models. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.eswa.2020.113661 | Expert Systems with Applications |
Keywords | DocType | Volume |
SARS-Cov-2,COVID-19,Coronavirus,Machine learning,Early stage symptom | Journal | 160 |
ISSN | Citations | PageRank |
0957-4174 | 5 | 0.54 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martuza Ahamad | 1 | 5 | 0.54 |
| Sakifa Aktar | 2 | 5 | 0.54 |
| Rashed-Al-Mahfuz | 3 | 5 | 0.54 |
| Shahadat Uddin | 4 | 12 | 2.82 |
| Pietro Liò | 5 | 550 | 99.98 |
| Haoming Xu | 6 | 11 | 2.65 |
| Matthew A. Summers | 7 | 5 | 0.87 |
| Julian Quinn | 8 | 9 | 6.83 |
| Mohammad Ali Moni | 9 | 41 | 16.64 |