# Artificial Intelligence in Virtual Telemedicine Triage:
## 3 Materials and Methods
### Creation of the AI Algorithm
#### 3.5.2 Dataset Adaptation
These categorization decisions were made in collaboration with health professionals, ensuring that the dataset accurately represented the clinical context of the symptoms (see Table 2). In addition to the aforementioned categorization decisions, the dataset was also carefully reviewed to ensure consistency and completeness of the symptom descriptions. This involved cross-referencing the symptom descriptions with standard medical terminologies to avoid ambiguity or overlap between symptoms. Moreover, where necessary, synonyms and related terms were added to the dataset to capture variations in how different symptoms may be described or referred to by patients. This meticulous approach to dataset curation not only improves the quality of the dataset but also helps to reduce the likelihood of misdiagnosis or misinterpretation of symptoms by the AI algorithm. Upon completion of dataset processing, the next step involves converting string variables to numeric variables to facilitate the use of mathematical algorithms in classification models. This is achieved by creating an equivalence table, as illustrated in Table 3, that maps the string variables to their corresponding numeric values. The conversion process \begin{table}
\begin{tabular}{c c} \hline \hline
**Symptoms** & **Categories** \\ \hline Nasal discharge & yes and no \\ Nasal congestion & yes and no \\ Sore throat & mild, moderate, and severe \\ Tearing & yes and no \\ Cough & dry and phlegmy \\ Sneezing & yes and no \\ Choking sensation & yes and no \\ Fever & yes and no \\ Joint pain & mild, moderate, and severe \\ Headache & mild, moderate, and severe \\ Nasal itching & yes and no \\ Swelling of the eyes & yes and no \\ Snoring & yes and no \\ Muscle pain & yes and no \\ Voice request & yes and no \\ Eye pain & yes and no \\ Diarrhea & yes and no \\ Nausea & yes and no \\ Belly pain & yes and no \\ Chest pain & yes and no \\ Loss of appetite & yes and no \\ Chills & yes and no \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Symptoms with the categories considered. applies to all the independent variables since mathematical algorithms only work with numerical inputs. Data pre-processing, including data cleaning, feature engineering, and conversion of variables, is critical in enhancing the accuracy and reliability of machine learning models. Therefore, it is essential to ensure that the data pre-processing stage is executed meticulously to minimize errors and biases that may affect the quality of the results obtained. It is important to note that the choice of the numeric representation of string variables can have a significant impact on the performance of the machine learning models. The selection of the most appropriate numeric representation requires careful consideration of the specific problem being addressed and the characteristics of the dataset. For instance, in some cases, one-hot encoding may be preferred over label encoding to avoid introducing ordinality or implying the magnitude of differences between the encoded variables. Furthermore, the choice of numeric representation can also affect the interpretability of the results obtained from the machine learning models.