Computerized hybrid decision-making system for hormone replacement therapy in menopausal women

The diversity of the results of different hormone replacement therapy (HRT) protocols and the fuzziness of the conclusions have caused problems in routine clinical practice.

To develop an intelligent decision-making system for HRT specifically is appropriate as we use the abbrevation HRT in the background section in menopausal women in order to assist physicians.

This study consisted of 179 peri- and post-menopausal patients who were admitted to Hacettepe University Hospital (between 1996 and 2001) with various menopausal complaints. Database variables used in this study were age, height, weight, menopause duration, clinical condition, HRT duration, and the laboratory test results. Our newly developed multiple-centered fuzzy clustering (MCFC) algorithm was applied to the medical data set to differentiate patient groups. Finally, a hybrid intelligent decision-making system was developed by combining knowledge-based algorithms and the MCFC algorithm results.

We have used Fuzzy C-means, K-means, Hard C-means, similarity based clustering, and MCFC algorithms on the medical data set and have determined that the MCFC algorithm is the most appropriate algorithm for our medical model. We have defined 5 clusters and 16 cluster centers. A diagnostic phrase was assigned to each cluster center by the physician and these clusters together with knowledge-based algorithms were used for the decision-making system.

We have developed a computerized hybrid decision-making system recommending HRT to peri- and post-menopausal women in order to assist and protect physicians.