heart disease prediction final year project cse
Abstract : Day by day the cases of heart diseases are increasing at a rapid rate and it’s very Important and concerning to predict any such diseases beforehand. This diagnosis is a difficult task i.e. it should be performed precisely and efficiently.
The research paper mainly focuses on which patient is more likely to have a heart disease based on various medical attributes. We prepared a heart disease prediction system to predict whether the patient is likely to be diagnosed with a heart disease or not using the medical history of the patient.
We used different algorithms of machine learning such as logistic regression and KNN to predict and classify the patient with heart disease. A quite Helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of Heart Attack in any individual.
The strength of the proposed model was quiet satisfying and was able to predict evidence of having a heart disease in a particular individual by using KNN and Logistic Regression which showed a good accuracy in comparison to the previously used classifier such as naive bayes etc.
A dataset is selected from the UCI repository with patient’s medical history and attributes. By using this dataset, we predict whether the patient can have a heart disease or not. To predict this, we use 14 medical attributes of a patient and classify him if the patient is likely to have a heart disease.
These medical attributes are trained under three algorithms: Logistic regression, KNN and Random Forest Classifier.
Most efficient of these algorithms is KNN which gives us the accuracy of 88.52%. And, finally we classify patients that are at risk of getting a heart disease or not and also this method is totally cost efficient.
Data Quality and Availability:
Limited Data: Access to comprehensive and high-quality medical datasets can be limited, especially if you're relying on publicly available sources.
Data Privacy: Health data is sensitive and often protected by regulations like HIPAA (in the U.S.). This can limit access to real-world data for academic purposes.
Data Preprocessing Challenges:
Data Cleaning: Health datasets often require extensive preprocessing to handle missing values, inconsistencies, and noisy data.
Feature Selection: Choosing the most relevant features from a large set can be challenging and crucial for model performance.
Algorithm Complexity:
Model Selection: Choosing the appropriate machine learning or statistical model can be difficult, and there’s no one-size-fits-all solution.
Tuning Parameters: Many models require careful tuning of parameters to achieve optimal performance, which can be time-consuming.
Interpretability:
Complex Models: Advanced models like deep learning can be highly accurate but may lack interpretability, making it difficult to understand how predictions are made.
Medical Interpretability: For clinical applications, it’s important to understand how features contribute to the predictions, which can be challenging with complex models.
This plays a key role for healthcare professionals in making accurate decisions and providing quality services to the public. The approach provided by the health care organization to professionals who do not have more knowledge and skills is also very important.
One of the main limitations of existing methods is the ability to draw accurate conclusions as needed.
In our approach, we are using different data mining techniques and machine learning algorithms, Naïve Bayes, k Nearest Neighbor (KNN), Decision tree, Artificial Neural Network (ANN), Random Forest to predict the heart disease based on some health parameters
Real-World Impact:
Health Benefits: Predictive models can potentially improve early diagnosis and treatment of heart disease, leading to better patient outcomes and potentially saving lives.
Public Health Contribution: Contributing to health data analytics can enhance public health initiatives and contribute to more effective disease management strategies.
Educational Value:
Practical Experience: Working on a heart disease prediction project allows you to apply theoretical knowledge from your coursework to a practical problem, bridging the gap between theory and practice.
Skill Development: You'll gain experience in key areas like data preprocessing, machine learning, statistical analysis, and model evaluation.
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