Prediction Equations in Spirometry Using Demographic and Spirometric Values
Major research in prediction equations for spirometry in adults take only demographic data i.e. gender, age, height and weight into its consideration. Furthermore, the studies conducted in India on the prediction equations for spirometry in adults are inadequate and old, to make their validity unreliable. We have developed prediction equations for spirometry in the adults of Pune in Maharashtra, India by considering their measured spirometric data in addition to the demographic data. For doing so, a dataset composed of 2092 healthy subjects from Pune, who underwent spirometry tests and whose results were recorded in the database was used. Linear, Quadratic and Logarithmic prediction equations were developed using the dataset for forced vital capacity, and 13 other parameters, and the accuracy was compared with one another as well as the existent equations. The results have denoted a significant increase in accuracy when compared to previous studies. The correlation between spirometric parameters is convincing to take into account for developing more specific & accurate predictions.
KeywordsData mining Data science Regression Prediction equations Accuracy Pulmonary disease Asthma Spirometry
We would like to thank BRIOTA Technologies Private Limited for their support and taking ownership of the research work presented in paper.
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