AI Based Medicine Recommendation System

The main idea of our Final Year Project is to develop an AI Based Medicine Recommendation System for the patient, inexperienced doctor, intern / medical student. We used three Machines Learning Classification Algorithms for recommendation that includes Decision Trees, Random Forest

Project Title

AI Based Medicine Recommendation System

Project Area of Specialization

Artificial Intelligence

Project Summary

The main idea of our Final Year Project is to develop an AI Based Medicine Recommendation System for the patient, inexperienced doctor, intern / medical student. We used three Machines Learning Classification Algorithms for recommendation that includes Decision Trees, Random Forest Algorithm and Support Vector Machine Algorithm. We take inputs from the users about their age, gender, blood pressure (low, normal, high), symptoms(Vomiting, Headache, Depression, Body Pain, Fatigue, Constipation, Shivering, Cough, etc.) . We will use an Algorithm out of these three that is best suited for the recommendation model for a better accuracy in the results.

Project Objectives

• This recommendation system potentially help patients to choose better medicines for their conditions/symptoms, and can give a benchmark to medicine providers such as doctors and pharmaceutical companies.

• By using the Machine Learning Classification Algorithms, we are aiming to achieve the accuracy from the model data (the data which is trained) to show better results in correspondence to user inputs which are given to that model.
• Provide relevant and accurate medicine recommendation to the users is our main objective.

Project Implementation Method

• For this Medicine Recommendation System we used three machine learning classification algorithms which are Decision Trees, Random Forest & Support Vector Machine (SVM).
• We are going to use a benchmark Data Set about medicine reviews which we are taken into our project.
• Then in the next step we use a python data analysis tool called Pandas to classify the relevant data from the raw data set files.
• After the data is cleaned, we then move to create the model by applying a different machine learning algorithms, but first we split the cleaned data into two phases, one is for the training of data and the other is for the testing of data in a 70:30 ratio set.
• The trained data label as model data, where the user's input carry out for the specific recommendation results.
• As for the front end part, we are going to use dart language which carry out on Flutter framework with Firebase for our application.

Benefits of the Project

• Our recommendation system which we are going to build have a potential  to help patients to choose better medicines according to their symptoms and give a useful tool to medicine providers.

• It can give the people a relative confidence on a specific medicine that they are not so much sure about whether they will use it or not.

• And for the pharmaceutical companies that recommendation system will eventually help in the growth of their productivity.

Technical Details of Final Deliverable

• A front end mobile application interface in which the end-user will give their specific response and in correspondence to that will get the specific recommendation.

• For data analysis of our medicine reviews dataset we used pandas which is a python library.

• We will apply three different machine learning classification algorithm by using python language as a backend.

Final Deliverable of the Project

Software System

Core Industry

IT

Other Industries

Medical , Health

Core Technology

Artificial Intelligence(AI)

Other Technologies

Others

Sustainable Development Goals

Good Health and Well-Being for People, Decent Work and Economic Growth

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com