DIGITAL PROTOTYPE FOR PREOPERATIVE RISK ASSESSMENT OF ANESTHESIA USING MACHINE LEARNING
Project development till Minor 1
Introduction Video
Project Brief
The underlying motivation of this project is based on the post-operative care of patients who have undergone major surgery.
Presently, the anesthesiologists use verbal methods to assess the risk factors associated with sedating the patient with anesthesia based on past medical history and present health condition.
The objective of proposed project is to develop an application to assess the risk. The algorithm will calculate the risk based on medical history and current test reports of the patient. The result will assist the user in determining whether going for the surgery is critical or not. The risk level will be determined as "Low Risk" or "High Risk".
We have collected datasets and performing its preprocessing and visualization. Using the Classification method, we have classified data into several groups based on age, gender and past and present medical conditions.
We are using Logistic Regression method to estimate the relationship between a dependent (target) and an independent variable (predictor).The results will be in binary format, which will used to predict the outcome of a categorical dependent variable.
Code Scripts
Challenges Foreseen
The main challenge we are experiencing in this project is to obtain datasets in the digital or editable form.
Missing data values cannot be interpreted by assumption or using any statistical method. Thus, there is limitation of parameters that are being used for developing the algorithm.
Reliability of datasets is a major concern as the data is obtained in handwritten form communicated verbally by the patient.
