Python
Aim:
To apply various machine learning algorithms to analyze medical data and predict the likelihood of heart and liver diseases, assisting healthcare professionals in making informed decisions for diagnosis and treatment.
Abstract:
Heart disease, often referred to as cardiovascular disease, remains a leading cause of mortality worldwide due to its impact on blood flow and vital organ function. Similarly, liver diseases result in over 2 million deaths globally each year, underscoring the urgent need for effective prediction and prevention strategies. This project leverages advancements in Artificial Intelligence and Machine Learning to design an integrated system capable of predicting both heart and liver diseases. By employing multiple machine learning classifiers such as Random Forest, Logistic Regression, Extra Tree Classifier and Support Vector Machines, the system aims to provide a comparative analysis of model performance.
The project involves data preprocessing, feature selection, and model optimization to enhance accuracy and robustness. Preliminary results indicate that this approach can achieve significant predictive accuracy, with the potential to serve as a decision-support tool for healthcare practitioners. The final outcome includes a web application where users can input data for real-time predictions, facilitating early diagnosis and better health management
Existing System:
Current systems for heart and liver disease prediction utilize machine learning models to analyze medical datasets, focusing primarily on individual diseases without integration or advanced optimization. These systems have demonstrated varying levels of accuracy: 82.35% on the Heart Disease dataset and 83.33% on the Liver Disease dataset. While these accuracies are promising, the existing approaches often lack robust preprocessing techniques, comprehensive feature selection, and comparative analysis of classifiers. Furthermore, they do not provide user-friendly interfaces for real-time prediction, limiting their practical application in healthcare settings.
Problem Definition:
Heart and liver diseases cause a significant number of deaths worldwide, making early detection crucial for saving lives. Existing systems struggle with low accuracy, limited ability to handle complex data, and a lack of tools that can predict both diseases effectively. They also fail to provide easy-to-use, real-time prediction platforms for patients and healthcare professionals. This project aims to address these issues by creating an accurate and accessible machine learning-based system for predicting heart and liver diseases.
Proposed System:
The proposed system aims to develop a machine learning-based solution to predict heart and liver diseases with high accuracy. For heart disease prediction, the system will leverage the Random Forest classifier, which has shown to provide the highest accuracy in previous studies. For liver disease prediction, the system will utilize the Extra Tree classifier algorithm, known for its superior performance in handling complex datasets.
The system will incorporate robust data preprocessing techniques such as handling missing values, normalization, and feature selection to improve the quality of predictions. Additionally, it will feature a user-friendly web application, allowing users to input medical data and receive real-time predictions. This integrated approach will provide healthcare professionals and patients with accurate, actionable insights, facilitating early diagnosis and improving overall health outcomes.
Advantage:
- High Accuracy: Reliable predictions using Random Forest and Gradient Boosting.
- Comprehensive: Predicts both heart and liver diseases.
- Real-Time: Instant predictions via a user-friendly web app.
- Scalable: Easily extendable for more diseases.
- Early Detection: Supports timely diagnosis and intervention.
- Cost-Effective: Reduces manual diagnostic efforts.
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