1 results listed
The research study predicts surgical operations
based on patient characteristics using different types of Learning
Vector Quantization algorithms. The primary goal is to identify
whether a patient requires surgery or not and classify the type of
surgery needed. The paper utilizes a disease dataset containing
many patient attributes, including disease-specific factors and
medical history to train and evaluate the models. Also, tested
types of LVQ algorithms including LVQ, RSLVQ, Soft LVQ
(SLVQ), Generalized LVQ (GLVQ), Fuzzy LVQ, and LVQ3.
Results show that GLVQ achieved the highest performance with
an accuracy of 98.42%, precision of 0.99, recall of 0.97, and F1-
score of 0.98. The discovery shows that advanced GLVQ can be
very useful in healthcare for making predictions. This model can
help doctors make better decisions by accurately predicting
whether a patient needs surgery.
International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES
Ali Asghar
Oğuz Findik
Emrah Özkaynak