Bayesian statistics as applied to hypertension diagnosis

The Bayesian approach was applied to a specific case of medical diagnosis, i.e. essential hypertension and five types of secondary hypertension (fibrodysplasic renal artery stenosis, atheromatous renal artery stenosis, Conn´s syndrome, renal cystic disease, and pheochromocytoma). Only blood pressures, general information and general biochemical data are taken into account. Nineteen items were finally selected through statistical investigation of the experimental data as being both discriminative and independent. The marginal density distributions of every item, and then joint density distribution functions were determined within six types of hypertension. The frequency of a given hypertension type within the hypertensive patients was used as prior probability of this state. The loss matrix was established by medical arguments. The expected loss corresponding to six possible decisions was calculated for all cases. Both the ratio of secondary hypertensions that could be inferred from this set of data (not including the results of complementary tests) and that of correct essential hypertension diagnosis provided to be satisfactory.