Selecting the Most Accurate Forecasting Method for Medical Diagnosis. Breast Cancer Diagnosis - A Case Study

Different methods are usually applied for medical diagnosis problems. Most of them are only based on expert knowledge and the results are provided by model-driven methods and they are built from inflexible mathematical expressions. In this paper we suggest a Data-Driven perspective to facilitate the medical expert labour on diagnosis tasks. Furthermore, this paper offers a step by step procedure to select the most accurate forecasting method depending on the nature of the variables and the structure problem constraints. To validate such a selecting procedure, we apply it to a breast cancer diagnosis problem as a real case study.

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