govaccinate

README

govaccinate

Description

An artificial intelligence model to determine a person's eligibility to receive a COVID-19 vaccination based on symptoms diagnosis against current health conditions using fuzzy logic written in Go.

Design

This is a project of my Artificial Intelligence learning exercise. The model built based on fuzzy logic method. Fuzzy logic itself is a union of fuzzification, inference and defuzzification.

Fuzzification

In fuzzification phase, the model converts 3 input variables (which are: age, systolic blood pressure and symptoms weight) into their corresponding fuzzy values. The model give weight for each COVID-19 symptom declared by WHO (World Health Organization), diagnose the user, summed all the weights and stored as symptoms weight variable. Each input variable will be broken down into 3 kinds of fuzzy values quantity, which means we got 9 fuzzy values in total. These are the images to illustrate the variables and their fuzzy chart:

Symptomps	Classification	Weight
Sore throat	Less Common	5
Headache	Less Common	3
Aches and pains	Less Common	5
Diarrhea	Less Common	3
Skin rash	Less Common	3
Irritated eyes	Less Common	3
Fever	Common	20
Cough	Common	25
Tiredness	Common	20
Loss of taste or smell	Common	20
Shortness of breath	Priority	45
Loss of speech or mobility	Priority	40
Chest pain	Priority	50
Total	----------------->	242

Inference

Then, the fuzzy values will be inferenced using 27 rules (3 x 3 x 3 possibilities) based on Mamdani method resulting a minima output for each rules and maxima output for each rule result. These are the rules applied to the model:

Age	Systolic Blood Pressure	Symptoms Weight	Urgency Level
Young	Low	Less	Low
Young	Low	Common	Low
Young	Low	Priority	Low
Young	Normal	Less	Medium
Young	Normal	Common	Medium
Young	Normal	Priority	Medium
Young	High	Less	Medium
Young	High	Common	Medium
Young	High	Priority	Medium
Mature	Low	Less	Low
Mature	Low	Common	Low
Mature	Low	Priority	Medium
Mature	Normal	Less	Low
Mature	Normal	Common	Medium
Mature	Normal	Priority	Emergency
Mature	High	Less	Low
Mature	High	Common	Low
Mature	High	Priority	Medium
Old	Low	Less	Low
Old	Low	Common	Low
Old	Low	Priority	Medium
Old	Normal	Less	Medium
Old	Normal	Common	Emergency
Old	Normal	Priority	Emergency
Old	High	Less	Medium
Old	High	Common	Low
Old	High	Priority	Low

The urgency level is not only determined by the symptoms felt but also determined by age and blood pressure. For example, if there is a 2 years old baby and has various symptoms, then they will not receive the vaccine because their body's immunity has not been able to adapt to the dose and chemical compounds contained in the vaccine. On the other hand, the same applies to people who have blood pressure that is too low or too high, they will not be prioritized until their blood pressure returns to normal.

Defuzzification

Lastly, the model perform a defuzzification based on centroid method to determine a crisp value as the end result of the whole program.

Development

The model developed using Go language. Code written in a very WET (write-everything-twice) way to point out clear rules definitions implemented in the model. The model implemented in real life scenario. There are some standard input checks implemented to avoid errors. Also note that the code is far from perfect, the author (well... me) is not an experienced software engineer... but I wrote all of this myself and I am pretty proud of it \m/.

Installation

▶ GO111MODULE=on go get -u -v github.com/gasfad01/govaccinate

Usage

▶ govaccinate

Copyright

Tugas Besar Kecerdasan Buatan - S1 Teknik Komputer Semester Genap 2020/2021

© Copyright All Rights Reserved - Bagas Fadillah Islamay (2021)