It is known that the data preparation phase is the most time consuming in the data mining process, using up to 50 % or up to 70 % of the total project time. Currently, data mining methodologies are of general purpose and one of their limitations is that they do not provide a guide about what particular task to develop in a specific domain. This paper shows a new data preparation methodology oriented to the epidemiological domain in which we have identified two sets of tasks: General Data Preparation and Specific Data Preparation. For both sets, the Cross-Industry Standard Process for Data Mining (CRISP-DM) is adopted as a guideline. The main contribution of our methodology is fourteen specialized tasks concerning such domain. To validate the proposed methodology, we developed a data mining system and the entire process was applied to real mortality databases. The results were encouraging because it was observed that the use of the methodology reduced some of the time consuming tasks and the data mining system showed findings of unknown and potentially useful patterns for the public health services in Mexico.This article is part of the Topical Collection on Systems-Level Quality Improvement

It is known that the data preparation phase is the most time consuming in the data mining process, using up to 50 % or up to 70 % of the total project time. Currently, data mining methodologies are of general purpose and one of their limitations is that they do not provide a guide about what particular task to develop in a specific domain. This paper shows a new data preparation methodology oriented to the epidemiological domain in which we have identified two sets of tasks: General Data Preparation and Specific Data Preparation. For both sets, the Cross-Industry Standard Process for Data Mining (CRISP-DM) is adopted as a guideline. The main contribution of our methodology is fourteen specialized tasks concerning such domain. To validate the proposed methodology, we developed a data mining system and the entire process was applied to real mortality databases. The results were encouraging because it was observed that the use of the methodology reduced some of the time consuming tasks and the data mining system showed findings of unknown and potentially useful patterns for the public health services in Mexico.