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GlucoGaurd: Smartphone Based Continuous Glucose Monitoring

Supervised By:
Imran Ihsan
Duration:
2019 - 2020
Client:
Funded By:
IGNITE NGIRI (PKR, 55,000)
Team:
Completed:
Project completed in 100%.
System Design
Training Results with Parameter Tuning
Precision on Classifiers
PR and ROC Curves

Project description

Diabetes Classification using Machine Learning Algorithms

Diabetes mellitus has affected 382 million people worldwide and there is an increasing number of people with type 2 diabetes in every region. Diabetes can cause many complications if left untreated. By integrating the difference between data sets and human intelligence, machine learning has made it possible for medical professionals to promote disease diagnostics. We may begin to implement machine learning strategies for classification in a data set that represents a community at high risk of developing diabetes. The sample for this research was the PIMA Indian population. Since 1965, the population has been under continuous study by the National Institute of Diabetes and Digestive and Kidney Diseases due to its high prevalence rates of diabetes. With the help of this dataset that we have collected on patients, we will be able to make accurate assumptions on how likely an individual is to suffer from the occurrence of diabetes and then take appropriate action. The goal of this project is to predict type 2 diabetes based on a dataset. It uses a machine learning model that is trained to predict diabetes mellitus before it hits. This is done using multiple machine learning algorithms to select which is best. Our study begins with a thorough look at how researchers who used the same dataset tackled the same question. This allowed us to develop an understanding of the data and prepare the way for our report, especially as the authors proposed alternate approaches worth studying in. Four machine learning classifiers are used to train multiple models that are used to predict positive or negative outcomes. BMI (Body Mass Index), Age (Age), Glucose Level, are ii given set of inputs. Based on those features, we predict whether or not a patient has diabetes.

Tools and Technologies

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