Design of knowledge acquisition model in glaucoma medical treatments recommender system  

Oleh:

Fiarni, Cut

Jenis: Article from Proceeding
Dalam koleksi: PROCEEDING The 8th International Seminar on Industrial Engineering and Management (8th ISIEM). Atria Hotel & Conference, Malang, Indonesia March 17 – 19, 2015, page DSS-40.
Topik: Glaucoma medical treatments; recomender system; knowledge management; data mining
Fulltext: Paper 48 Cut Fiarni - Harapan Bangsa Institute of Technology.pdf (355.66KB)

Isi artikel

Glaucoma is a progressive eye disease that damages the optic nerve, usually associated with increased intraocular pressure (IOP), that if left untreated, it can lead to blindness. Glaucoma is affecting round 67million people all over the world. In Indonesia it is the second major cause of blindness, with an incidence varies from 0.4 % to 1.6%.There are several option medical treatment for glaucoma, with varying risk and benefit. Before taking any medical treatments, patient or their medical proxy facing the biggest task, which is to decide the best treatment regarding their benefits, risks, cost and the outcome of treatment. In order to make the best suitable treatment, the patient not only need to inform medical option and advice from medical expert but also from other people who already facing the same dilemma. And because we live in a digital era where data and information is scattered and overload, so instead of getting the related knowledge needed, patient get confused and take more time in deciding the best treatment. Sometime it could lead to health condition worsening.This paper proposed a model of the medical treatment recommender system, which could assist patients and their medical proxy in deciding the best treatment and also could share medical information and knowledge needed related to the decision. We propose to explore the ability of knowledge management to provide the decision maker with appropriate technologies, strategies and process to turn data and information into valuable knowledge to make decisions. We will also use classification technique as SVM to make recommended treatment based on extracted rules from data and information. The main methodology is to identify the important factors and the similarities based on classification rules to be extracted. The contribution to these research areas is to analyze the suitable model for the proposed system.

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