This project is an interactive dashboard showing 3 models used in the prediction of the recurrence of Thyroid Disease. This was done in R utilizing flexdashboard for the interacive element. The data was collected from the UCI Machine Learning Repository and provided by Kaggle. The data features are as follows:
Age: The age of the patient at the time of diagnosis or treatment.
Gender: The gender of the patient (male or female).
Smoking: Whether the patient is a smoker or not.
Hx Smoking: Smoking history of the patient (e.g., whether they have ever smoked).
Hx Radiotherapy: History of radiotherapy treatment for any condition.
Thyroid Function: The status of thyroid function, possibly indicating if there are any abnormalities.
Physical Examination: Findings from a physical examination of the patient, which may include palpation of the thyroid gland and surrounding structures.
Adenopathy: Presence or absence of enlarged lymph nodes (adenopathy) in the neck region.
Pathology: Specific types of thyroid cancer as determined by pathology examination of biopsy samples.
Focality: Whether the cancer is unifocal (limited to one location) or multifocal (present in multiple locations).
Risk: The risk category of the cancer based on various factors, such as tumor size, extent of spread, and histological type.
T: Tumor classification based on its size and extent of invasion into nearby structures.
N: Nodal classification indicating the involvement of lymph nodes.
M: Metastasis classification indicating the presence or absence of distant metastases.
Stage: The overall stage of the cancer, typically determined by combining T, N, and M classifications.
Response: Response to treatment, indicating whether the cancer responded positively, negatively, or remained stable after treatment.
Recurred: Indicates whether the cancer has recurred after initial treatment.
The full report can be found here main.md