Implementation Of Svm And Nb Algorithms For Classification Of Diseases And Their Treatments
Now days, the standard healthcare system mainly depends on the delivery of modern and efficient healthcare
services. Due to time and cost constraints, most of the people rely on healthcare system to obtain healthcare services.
Healthcare system becomes very important to develop an automated tool that is capable of identifying and discriminating
relevant healthcare information. This will not only help common person but also to doctors to update their knowledge and have
correct treatment of diseases. The medical field is one of the field in which new research is carried out at a faster rate. In a
medical field automation is gaining momentum. From the medical data useful information can be extracted and made useful
for generating software’s or MEDLINE applications that can help doctors in the treatment. In this paper, we have to identify
diseases and their treatments in short text using SVM and NB classification algorithms. Experimental results show that SVM
gives better classification result than naïve bayes. These algorithms are evaluated using four criteria: Accuracy, Precision,
Recall and F-measure.
Index Terms — MEDLINE, SVM,NAÏVEBAYES, NLP, HEALTHCARE DATASETS, DISEASES AND TREATMENT