Modelo de reconhecimento de padrão na relação da insuficiência da veia safena interna com o quadro clínico na doença venosa crônica - Jóia, André Amaral (2018)
Chronic Venous Disease (CVD) is a change in the functioning of the venous system caused by valve incompetence and affecting the superficial, deep venous system or both. This dysfunction provides venous reflux and, consequently, venous hypertension. Venous reflux plays a crucial role in the magnitude of symptoms and signs, ranging from simple feeling of weight and tiredness, varicose veins and even chronic ulcers. However, the presence of varicose veins in the lower limbs may be the main component. Some situations seem to contribute to the onset of the disease as predisposing factors: obesity, female sex, pregnancy and number of pregnancies, increase of age, contraceptive use and hormone replacement. CVD affects people all over the world with high prevalence and is the most common vascular disease, being the daily routine of the specialist. The suffering of the patient entails damages such as loss of work capacity, social isolation and changes in psychic conditions. The treatment presents great challenges, not because of its complexity, but because of failures that begin in the SUS campaigns, in the orientation and in the care with the patient. Finally, there is a lack of incentives to increase the enthusiasm of physician specialists. This study aims to translate the CVD into a model of pattern recognition in the relation of the Great Saphenous Vein (GSV) insufficiency with the clinical picture, associating the degree of the disease with the indication of the treatment, in order to avoid complications. For the sample, 91 patients underwent Doppler vascular ultrasonography were selected, in a total of 117 lower limbs presenting GSV insufficiency and, mandatorily, with femoral saphenous junction insufficiency. All of them were classified clinically by CEAP and divided into two groups, Group_0 (mild disease - C2 and C3) and Group_1 (severe disease (C4, C5 and C6). Logistic Regression and Discriminant Analysis were used to treat the data, such as methods of recognition and classification. The leave-one-out technique was used to get the actual error in the rating rates of the models. All generated models presented satisfactory results having an average rating superior to 80% of hits. After the observation, the sociodemographic profile found was a female patient, multiparous, with age around 50 years old, with tendency to obesity, of low income, low schooling and advanced disease (C4,5,6).