Can the Plantar Pressure and Temperature Data Trend Show the Presence of Diabetes? A Comparative Study of a Variety of Machine Learning Techniques

This study aimed to explore the potential of predicting diabetes by analyzing trends
in plantar thermal and plantar pressure data, either individually or in combination, using various
machine learning techniques. A total of twenty-six participants, comprising thirteen individuals
diagnosed with diabetes and thirteen healthy individuals, walked along a 20 m path. In-shoe plantar
pressure data were collected and the plantar temperature was measured both immediately before and
after the walk. Each participant completed the trial three times, and the average data between the trials
were calculated. The research was divided into three experiments: the first evaluated the correlations
between the plantar pressure and temperature data; the second focused on predicting diabetes using
each data type independently; and the third combined both data types and assessed the effect of such
to enhance the predictive accuracy. For the experiments, 20 regression models and 16 classification
algorithms were employed, and the performance was evaluated using a five-fold cross-validation
strategy. The outcomes of the initial set of experiments indicated that the machine learning models
were significant correlations between the thermal data and pressure estimates. This was consistent
with the findings from the prior correlation analysis, which showed weak relationships between
these two data modalities. However, a shift in focus towards predicting diabetes by aggregating
the temperature and pressure data led to encouraging results, demonstrating the effectiveness of
this approach in accurately predicting the presence of diabetes. The analysis revealed that, while
several classifiers demonstrated reasonable metrics when using standalone variables, the integration
of thermal and pressure data significantly improved the predictive accuracy. Specifically, when only
plantar pressure data were used, the Logistic Regression model achieved the highest accuracy at
68.75%. Those predictions based solely on temperature data showed the Naive Bayes model as the
lead with an accuracy of 87.5%. Notably, the highest accuracy of 93.75% was observed when both the
temperature and pressure data were combined, with the Extra Trees Classifier performing the best.
These results suggest that combining temperature and pressure data enhances the model’s predictive
accuracy. This can indicate the importance of multimodal data integration and their potentials in
diabetes prediction