Abstract
Gestational diabetes is a type of high blood sugar that develops during pregnancy. It can occur at any stage of pregnancy and cause problems for both the mother and the baby, during and after birth. The risks can be reduced if they are early detected and managed, especially in areas where only periodic tests of pregnant women are available. Intelligent systems designed by machine learning algorithms are remodelling all fields of our lives, including the healthcare system. This study proposes a combined prediction model to diagnose gestational diabetes. The dataset was obtained from the Kurdistan region laboratories, which collected information from pregnant women with and without diabetes. The classification methods such as decision tree, random forest, SVM, KNN, logistic regression and deep learning methods are used for comparative prediction.
Existing System
In the existing systems they have discovered system to detect the diabetes by investigating and examining the patterns originate in the data via classification analysis by using Decision Tree and Naive Bayes algorithms which was a carried out by Iyer, Aiswarya & Jeyalatha, s & Sumbaly, Ronak. (2015). Diagnosis of Diabetes Using Classification Mining Techniques. It is concluded, that using PIMA dataset and cross validation approach the project concluded that J48 algorithm gives an accuracy rate of 74.8% while the naive Bayes gives an accuracy of 79.5% by using 70:30 split data.
In another system, they built predictive models using logistic regression and Gradient Boosting Machine techniques. They have used AROC (Area under the receiver operating characteristic curve) was used to evaluate the discriminatory capabilities of these models. But the GBM model accuracy was 84% and logistic Regression model was 83%. In existing method , the classification and prediction accuracy is not so high. Existing method for diabetes detection uses lab test such as fasting blood glucose and oral glucose tolerance. However , this method is time consuming .
Proposed System
In this project, algorithms namely logistic regression and Random forest algorithm. The model is trained on a dataset which have data of patients (Number of pregnancies, Plasma Glucose level, Blood Pressure level, Skin Thickness level(SAT), Insulin level, BMI, Diabetes Pedigree Function, Age).
The model selection is used to split the train and test data. Then the algorithm is employed on the dataset, after which data divided into “tested- negative” or “tested- positive” depending on the final result.
Gestational Diabetes finder is compared between Random forest algorithm and Logistic Regression and then the accuracy level is visualized and then the model is used for predicting early stage of GDM.
We finally developed the project with high precision accuracy algorithm. The prediction is much better and accurate when compared to other ML algorithms.
Hardware Requirements
- Processor : Intel Core i3 (or above)
- System Type : 64-bit Operating System
- Storage : 500 GB HDD / SSD
- RAM : 4 GB (minimum)
Software Requirements
- Operating System : Windows 10 (or above)
- Software : Anaconda Navigator, Python IDE
- Libraries: Pandas, NumPy, Matplotlib / Seaborn, Scikit-learn, XGBoost
- Programming Language : Python
Architecture Diagram
Project Modules
- Importing Libraries & Reading Data Module
- Summary of the data
- Visualization Module
- Decision Tree & Random Forest Module
- KNN & Logistic Regression Module
- Deep Learning & SVM Module
Components of Project Report
- Abstract
- Table of Contents
- List of Tables
- List of Figures
- Chapters
- Introduction
- Literature review
- Problem definition and requirement analysis
- Design and Implementation
- Testing and deployment
- Future enhancements
- Summary
- References
Project Report Pages : 80
Can be used in : Python
Delivery Time : Within 2 hours.
Support / Query : Call +91-7449000533
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