IEEE BASE PAPER TITLE:
Effective Feature Engineering Technique for Heart Disease Prediction With Machine Learning
OUR PROPOSED PROJECT TITLE:
Heart Disease Prediction With Machine Learning
IEEE BASE PAPER ABSTRACT:
Heart failure is a chronic disease affecting millions worldwide. An efficient machine learning based technique is needed to predict heart failure health status early and take necessary actions to overcome this worldwide issue. While medication is the primary treatment, exercise is increasingly recognized as an effective adjunct therapy in managing heart failure. In this study, we developed an approach to enhance heart failure detection based on patient health parameter data involving machine learning. Our study helps improve heart failure detection at its early stages to save patients’ lives. We employed nine machine learning based algorithms for comparison and proposed a novel Principal Component Heart Failure (PCHF) feature engineering technique to select the most prominent features to enhance performance. We optimized the proposed PCHF mechanism by creating a new feature set as an innovation to achieve the highest accuracy scores. The newly created dataset is based on the eight best-fit features. We conducted extensive experiments to assess the efficiency of several algorithms. The proposed decision tree method outperformed the applied machine learning models and other state-of-the-art studies, achieving a high accuracy score of 100%, which is admirable. All applied methods were successfully validated using the cross-validation technique. Our proposed research study has significant scientific contributions to the medical community.
PROJECT OUTPUT VIDEO:
ALGORITHM / MODEL USED:
Random Forest Classifier, Bagging Classifier, XG Boost & LightGBM.
OUR PROPOSED PROJECT ABSTRACT:
The project titled “Heart Disease Prediction With Machine Learning” represents a comprehensive exploration into the realm of predictive analytics, aimed at enhancing the accuracy and reliability of heart disease diagnosis. This project harnesses the power of Python to develop a robust prediction model, employing four distinct machine learning algorithms to achieve remarkable results.
Four prominent machine learning algorithms, namely the Random Forest Classifier, Bagging Classifier, XG Boost, and LightGBM, were meticulously implemented and evaluated in this project. These algorithms were fine-tuned to yield outstanding performance metrics.
The Random Forest Classifier, after rigorous training and testing, achieved an impressive 100% accuracy in both the training and test datasets. The Bagging Classifier, in a similar vein, demonstrated exceptional predictive capabilities with a perfect 100% accuracy on both training and test data. The XG Boost model and LightGBM, known for their efficiency, also excelled by achieving a flawless 100% accuracy in both training and test data.
The dataset used in this project comprises a substantial 1025 records, each containing 14 distinct features. The richness and diversity of this dataset contribute to the project’s reliability and robustness.
In conclusion, “Heart Disease Prediction With Machine Learning” stands as an exemplary demonstration of the prowess of machine learning in medical diagnostics. The exceptional results obtained by employing four different models with 100% accuracy on both training and test datasets underline the potential of this approach in revolutionizing heart disease diagnosis and treatment. This project paves the way for further advancements in predictive healthcare analytics and sets a high standard for future research in the domain.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED.
- Input Devices : Keyboard, Mouse.
- Ram : 8 GB.
- Operating system : Windows 10 Pro.
- Coding Language : Python 3.10.9.
- Web Framework : FLASK.
AZAM MEHMOOD QADRI, ALI RAZA, KASHIF MUNIR, AND MUBARAK S. ALMUTAIRI, “Effective Feature Engineering Technique for Heart Disease Prediction With Machine Learning”, IEEE Access, (Volume: 11), 2023.