The document discusses the application of machine learning techniques, including logistic regression, k-nearest neighbors, support vector machines, and extreme gradient boosting, to improve the diagnosis of cardiovascular diseases. It emphasizes the importance of early and accurate diagnosis for effective treatment and highlights the role of hyperparameter optimization through GridSearchCV in enhancing model performance. The research utilizes various datasets to enhance diagnostic accuracy and address class imbalances in heart disease prediction.

1. Efficient Medical Diagnosis of Human Heart
Diseases Using Machine Learning Techniques
With and Without GridSearchCV
PRESENTED BY: V LALITHA
Authors: Ghulab Nabi Ahmad, Hira Fatima, Shafiullah, Abdelaziz Salah and Imadadullah
IEEE Access

2. Introduction
• Cardiovascular diseases are a significant global health concern, accounting for a substantial portion of annual
deaths. Early and accurate diagnosis of heart diseases is essential for effective treatment and management.
• Machine learning has emerged as a valuable tool for medical diagnosis, offering the potential for improved
accuracy and efficiency.
• The research paper focuses on utilizing machine learning techniques to enhance the diagnosis of human heart
diseases.
• The primary aim is to improve the accuracy of heart disease diagnosis using a variety of techniques and
hyperparameter optimization.

3. Model’s Flow Diagram
1. Data Collection:
They are Kaggle's Heart Disease Cleveland, Hungary, Switzerland & Long Beach V dataset, Heart Disease UCI Kaggle dataset.
2. Data Pre-Processing:
Data CleaningData TransformationFeature SelectionData Splitting, Handle Class Imbalance.
3. Data Mining:
It generally refers to the process of extracting valuable insights, patterns, and knowledge from large
datasets, particularly related to human heart diseases.
4. Proposed Model:
The proposed model involves the application of machine learning algorithms like Logistic Regression, k-Nearest Neighbors (K-NN),
Support Vector Machine (SVM), and XGBoost.
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4. Dataset Information

5. Heart Disease Prediction Proposed Model
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6. Logistic Regression
Logistic Regression is primarily used for binary classification problems, where you want to predict one of two
possible outcomes.
1. Binary Classification: Logistic Regression is employed when you want to classify data into one of two classes,
such as Yes/No, True/False, Spam/Not Spam, or 1/0.
2. Model: It uses a logistic (sigmoid) function to transform a linear combination of input features into a
probability score between 0 and 1.
3. Probability: The logistic function maps the output to a probability, where values closer to 1 indicate a high
probability of belonging to one class, and values closer to 0 indicate a high probability of belonging to the other
class.

7. K-Nearest Neighbours (KNN)
•K-Nearest Neighbors (KNN) is a machine learning algorithm for classification and regression.
• It predicts outcomes by comparing a new data point with its k-nearest neighbors from the training data.
•The choice of k is very important in KNN.
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8. Support Vector Machine
Support Vector Machine (SVM) is a powerful supervised machine learning algorithm used for classification and
regression tasks.
1. Objective: SVM aims to find the hyperplane that best separates different classes in the feature space while
maximizing the margin between the classes.
2. Margin: The margin is the distance between the hyperplane and the nearest data points from each class. SVM
looks for the hyperplane with the largest margin.
3. Kernel Trick: SVM can handle both linear and non-linear data by using kernel functions to map data into
higher-dimensional spaces.

9. Extreme Gradient Boosting
Extreme Gradient Boosting, often referred to as XGBoost, is a powerful and popular machine learning algorithm
that belongs to the ensemble learning family, specifically gradient boosting.
1. Ensemble Learning: XGBoost is an ensemble learning technique, which means it combines the predictions of
multiple weaker models (typically decision trees) to create a strong predictive model. This ensemble approach
helps improve predictive accuracy.
2. Gradient Boosting: XGBoost employs a gradient boosting framework, which is a sequential training method. It
builds a series of decision trees where each tree corrects the errors made by the previous one. This leads to a
more accurate and robust model.

10. GridSearchCV
• Grid Search Cross-Validation (GridSearchCV) is a hyperparameter tuning technique used in machine learning to
systematically search for the best combination of hyperparameter values for a given model.
1. Define the Hyperparameter Grid:
First, you need to specify the hyperparameters that you want to tune and the range of values you want to test.
For each hyperparameter, create a list of possible values to explore. This defines the hyperparameter grid.
2. Choose a Model:
Select the machine learning algorithm you want to use. This could be a classifier or a regressor, depending on
your problem.

11. GridSearchCV
3. Split the Data:
Divide your dataset into two parts: A training set for model training and a validation set for hyperparameter
tuning. The validation set helps assess the performance of different hyperparameter combinations.
4. Perform Cross-Validation:
GridSearchCV uses k-fold cross-validation to evaluate each combination of hyperparameters. It divides the training
data into k subsets (folds) and iterates through them, using one as the validation set while training on the other k-
1 sets.

12. Hyperparameters
• The best hyper parameter of XG BOOST with optimization technique for Kaggle’s heart disease Cleveland,
Hungary, Switzerland & long beach V dataset.
• The best hyperparameter of XG BOOST with optimization technique for heart disease UCI Kaggle dataset.
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13. With VS Without GridSearchCV
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14. Thank You