Investigating the Impact of Foot Pain on the Lifestyle using Statistical and Machine Learning Approaches

Statistical & Machine Learning analysis of Impact of Foot Pain on the Lifestyle

Abstract

Foot pain and problems have a direct impact the quality of human life. Such issues may affect the foot and other associated functions. The foot problems can be caused due to multiple factors. The use of predictive algorithms utilizing the electronic health records of the patients and data acquired from other sources is helping the healthcare industry to develop tools that are very helpful in diagnosis and prognosis. Professional physiotherapists have been consulted for restructuring of Foot Health Status Questionnaire (FHSQ). Machine learning model and foot disorder predictive algorithm based on the real-world data collected from the foot disorder patients has been developed that is able to accurately diagnose the foot problem of patient. The machine learning methods Gaussian Naive Bayes (GaussianNB), Random Forest, XGBoost, Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT) and AdaBoost have been applied to predict foot disorders. A statistical analysis has been performed for finding the association between foot disorders and other variables. The results of the study have provided useful results about the association of variables and the frequency charts and tables provide useful description of general trends of the society towards the foot functionality and problems as well. Strong association is found among various factors and foot pain.