Optimizing Machine Learning-Based Ovarian Cancer Prediction Through Normalization Strategie
Objective
Ovarian cancer prediction remains a critical challenge in medical diagnosis. This project aims to enhance ovarian cancer prediction by utilizing multiple machine learning algorithms, including Decision Tree, Random Forest, LightGBM, XGBoost, CNN, and GRU. The dataset used is from Kaggle, containing medical features essential for ovarian cancer detection. By applying Gaussian metrics for data balancing and RobustScaler for feature scaling, the project optimizes the model’s performance. The system performs binary classification to predict the likelihood of ovarian cancer. The backend is built using Flask, while the front-end leverages HTML, CSS, and JavaScript for ease of use. This approach improves prediction accuracy and scalability, providing a reliable tool for early diagnosis.
Abstract
Ovarian cancer prediction remains a critical challenge in medical diagnosis. This project aims to enhance ovarian cancer prediction by utilizing multiple machine learning algorithms, including Decision Tree, Random Forest, LightGBM, XGBoost, CNN, and GRU. The dataset used is from Kaggle, containing medical features essential for ovarian cancer detection. By applying Gaussian metrics for data balancing and RobustScaler for feature scaling, the project optimizes the model’s performance. The system performs binary classification to predict the likelihood of ovarian cancer. The backend is built using Flask, while the front-end leverages HTML, CSS, and JavaScript for ease of use. This approach improves prediction accuracy and scalability, providing a reliable tool for early diagnosis.
Keywords:
Ovarian cancer, machine learning, prediction, data balancing, RobustScaler, Decision Tree, XGBoost, CNN, GRU, Flask.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
1. SOFTWARE REQUIREMENS
Operating System : Windows 7/8/10
Server-side Script : HTML, CSS, Bootstrap & JS
Programming Language : Python
Libraries : Flask, Pandas,, Sklearn,NumPy, Seaborn, Matplotlib,tensorflow
IDE/Workbench : VSCode
Technology : Python 3.8+
Server Deployment : Xampp Server
Database : MySQL
HARDWARE REQUIREMENTS
Processor - I5/Intel Processor
RAM - 8GB+ (min)
Hard Disk - 128 GB+
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - Any
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