Heart Disease Prediction Using Machine Learning Algorithms Optimized with GridSearchCV

Описание: Heart Disease Prediction Using Machine Learning Algorithms Optimized with GridSearchCV
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Abstract :
Predicting cardiac disease remains one of the most complex challenges in the medical field. It takes a lot of time and effort to figure out what’s causing this, especially for doctors and other medical experts. This project addresses this challenge by applying various machine learning algorithms to predict cardiac disease, specifically LR, KNN, SVM, and GBC. The project utilizes GridSearchCV for hyperparameter optimization and employs a 5-fold cross-validation technique to ensure robust model evaluation.

Objectives:
Construct an intuitive and accurate heart disease prediction system using modern machine learning techniques. Implement and compare the performance of various machine learning classifiers, including LR, K-NN, SVM, and GBC. Utilize GridSearchCV for hyperparameter optimization to enhance the performance of the machine learning models.

Existing System:
Use of machine learning (ML) is a solution to reduce and understand the symptoms related to heart disease. The existing system employs models based on Chi-Square tests and PCA for dimensionality reduction in the detection of heart disease. Chi-square test (CHI) sorts features based on the class and filters out the top features on which depends the class label
PCA determine the number of meaningful components to be retained.
Random Forest (RF) classifiers are used to classify heart disease.

Proposed System:
The proposed system aims to enhance the accuracy and reliability of heart disease prediction using advanced machine learning techniques
Proposed system highlights the importance of hyperparameter tuning through GridSearchCV. Apply feature selection strategies, including correlation-based feature subset evaluation and GBC with GridSearchCV
Use GridSearchCV to systematically search for and select the best hyperparameters for each model. Train and optimize multiple machine learning models to determine the most effective approach for heart disease prediction.

Advantages :
Minimizes overfitting and provides a more accurate evaluation of the model's performance
Reduces computational complexity
improved prediction accuracy
Enhances the model's robustness

Modules:
Pre-processing module
Feature Extraction module
Random Forest module
GridSearchCV module

#ML for Medical Diagnosis #AI Health Prediction System #KNN SVM Decision Tree Example #Best ML Algorithm for Heart Disease # Data Science Final Year Project # AI Predictive Model for Healthcare

#Heart Disease Prediction using ML #GridSearchCV Optimization #Final Year Project #AI in Healthcare

In this video, we explore a real-world Machine Learning project to predict heart disease using multiple ML algorithms such as Logistic Regression, Decision Tree, Random Forest, and SVM, optimized with GridSearchCV. Perfect for final-year students, data science beginners, and those interested in AI in healthcare.