Interview survey is one of the options for investigations with light loads on participants to study how people fall compared with measurements by many sensors. In this paper, we aimed at predicting fall patterns from interview text data. We use k-means clustering method to confirm the validity of the labels attached to the interview data, and also confirmed the validity of the summaries of the interview data by interviewer researchers by focusing on the co-occurrence word analysis. After confirming the validity of the labels and summaries, we construct a naive Bayes model classifiers to classify the fall patterns. The average classification rate was 61.1% for 3 types of falls - falls by an unexpected external force, by losing balance or supports, and by other reasons.
