Because of physiological monitoring data has time continuity, inaccuracy, and fuzziness, the traditional classification algorithm is difficult to be used directly. In view of the above problems, a classification method of chronic lesions based on rough sets is proposed. First, the physiological monitoring data are discretized based on fusion of correlation and Chi-merge statistics. Then, this method uses the attribute reduction algorithm based on the compatibility matrix to remove the redundant attributes of the data. Finally, classification rules are mined based on batch and incremental data, and intelligent classification of chronic diseases can be realized by applying the above rules based on MapReduce framework. Experiments show that the method has a high recognition rate, which is helpful for the individual to fully understand the health risks.