Fuzzy-Based Application Model and Profile Matching for Recommendation Suitability of Type 2 Diabetic

Agus Wantoro, Admi Syarif, Kurnia Muludi, Khairun Nisa Berawi

Abstract


Diabetes Mellitus (DM) is a metabolic disease characterized by hyperglycemia due to insulin secretion abnormalities and a global health threat. DM has several types, namely type 1, 2, gestational, and other types. Type 2 diabetes patients have the largest number in the world. DM therapy can be done in 2 (two) ways: improving lifestyle and administering drugs. The problems and risks in recommending drugs are essential in the patient's healing process because they are likely to take medicine for life. Approximately 260,000 patients with type 2 diabetes experienced medication errors in 2017. The doctor's mistake in recommending drugs causes a long healing process and costs more. Recommending drugs requires pharmacological knowledge, and not all hospitals have pharmacologists. Several researchers have researched recommendations for antidiabetic drugs, but no studies have yet been found that discuss recommendations for combination antidiabetic drugs for type two to determine dosage and frequency. The number of medications used is 6 to 7, with many parameters 5 to 8. The latest endocrinology guidelines for 2020 state that in recommending antidiabetic drugs, not only 6 to 7 participants, but still need to maintain other aspects. Therefore, this study aims to build an expert system model with a new approach in recommending antidiabetic drugs with more complete parameters and recommend dosage and frequency. The model developed uses the Fuzzy Profile Matching method. Fuzzy is used to calculate the suitability between the patient's condition and the type of antidiabetic drug. Profile Matching is used to calculate the core factor and secondary factor to obtain each drug's total value. The dose was calculated using the FIS Tsukamoto for inputting low dosage, and high dosage calculated the weighted average value. Determination of frequency using the IF-Then function. Model evaluation is done by comparing recommendation data from doctors. The results of the evaluation of the model obtained an accuracy of 90%. This system will reduce medical personnel errors in recommending antidiabetic drugs that can positively impact patients' time, the healing process, and costs. This study provides knowledge that antidiabetes drugs' determination requires many parameters, while other studies used only 4 to 8. This study also provides an overview of the dosages of drugs that drug companies can produce. Usually, the company only makes low and high dosage. This study shows that creating multiple drug dosage is more efficient for patients.

Keywords


Model evaluation; diabetic type 2; fuzzy Tsukamoto; profile matching; drugs; dosage; frequency.

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DOI: http://dx.doi.org/10.18517/ijaseit.11.3.12277

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