This repository contains a machine learning project focused on predicting heart disease. The goal of this project is to develop a model that can accurately classify whether a person is likely to have heart disease based on various medical attributes. The data have been abstacted from UCI Mschine Learning repository. This README file provides an introduction to the project, outlines its objectives and provides a brief summary to the group project.
Heart disease is one of the leading causes of death worldwide, and early detection plays a crucial role in improving patient outcomes. Machine learning techniques have shown great promise in predicting and diagnosing heart disease based on patient data. By leveraging machine learning algorithms and analyzing relevant features, we can create a predictive model that aids in identifying individuals at risk and provides actionable insights for healthcare professionals.
This project aims to develop an accurate heart disease prediction model by utilizing a diverse set of features such as age, resting blood pressure, cholesterol levels, and other relevant medical indicators. By training a machine learning model on a labeled dataset, we can enable healthcare providers to proactively identify patients at higher risk and initiate appropriate preventive measures or treatment plans.
Our motivation to persue this project was:
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Data Collection and Exploration: Gather a comprehensive dataset related to heart disease, including relevant features and associated labels. Perform exploratory data analysis to gain insights into the dataset, identify patterns, and understand the relationships between different attributes.
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Preprocessing and Feature Engineering: Cleanse and preprocess the dataset to handle missing values, outliers, and ensure uniformity. Perform feature engineering to derive new features or transform existing ones that can enhance the predictive power of the model.
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Model Development, Evaluation and Fine-tuning: Utilize various machine learning algorithms, such as logistic regression, decision trees, random forests, support vector machines (SVM), or neural networks, to develop predictive models for heart disease classification. Compare the performance of different algorithms and choose the most suitable one based on evaluation metrics and domain knowledge. Assess the performance of the developed model using appropriate evaluation metrics such as accuracy, precision, recall, and F1 score. Fine-tune the model by adjusting hyperparameters or exploring ensemble methods to optimize its performance.
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Documentation and Sharing: Create comprehensive documentation explaining the project's methodology, datasets used, preprocessing techniques, model selection, and evaluation. Share the project code, trained model, and relevant resources on a public repository like GitHub to foster collaboration, reproducibility, and knowledge sharing.
By achieving these objectives, we aim to contribute to the field of healthcare by providing an accurate and efficient tool for heart disease prediction using machine learning techniques. It is important to note that this project is for research and educational purposes and should not replace professional medical advice or diagnosis.
- K-Nearest Neighbours
- C-Support Vector
- Decision Tree
- Random Forest Algorithm
- AdaBoost Boosting technique
- Naive Bayes