Predicting Heart Disease in Patients: An Analysis of KNN, Decision Trees, and Random Forest Algorithms
QUESTION
Guidelines
- Write you response as a research analysis with explanation and APA Format
- Share the code and the plots
- Put your name and id number
- Upload Word document and ipynb file from google colab
HW04 Cover Sheet – Analyze the following dataset
- Apply the KNN, Decision Tree and Random Forest algorithm on the iris data set
- Apply KNN algorithm work with iris dataset (knn_iris.ipynb)
- Plot the Decision tree and feature importance for iris dataset (Decisiontree_Randomforest.ipynb)
- Heart Disease Dataset
Deliverable – your own research paper with analysis
Predict heart disease in patients –
https://archive.ics.uci.edu/ml/datasets/Heart+Disease
https://www.kaggle.com/ronitf/heart-disease-uci
Start with exploratory data analysis
- KNN
- Decision Trees
- Random Forest
You should have your own conclusions and references in the end
ANSWER
Predicting Heart Disease in Patients: An Analysis of KNN, Decision Trees, and Random Forest Algorithms
Abstract
This research paper aims to predict the presence of heart disease in patients using machine learning algorithms, specifically KNN (K-Nearest Neighbors), Decision Trees, and Random Forest. The analysis is conducted on the Heart Disease dataset, sourced from the UCI Machine Learning Repository and Kaggle. The paper begins with exploratory data analysis to gain insights into the dataset and understand the variables’ relationships. Subsequently, the three algorithms are applied and evaluated for their predictive performance. Conclusions are drawn based on the results obtained, highlighting the strengths and limitations of each algorithm.
Introduction
Heart disease is a critical health concern affecting individuals worldwide. Early detection and accurate prediction play a crucial role in managing and preventing adverse outcomes. This research aims to utilize machine learning algorithms to predict heart disease in patients based on various clinical and demographic features.
Exploratory Data Analysis
Prior to applying the machine learning algorithms, exploratory data analysis is performed to understand the dataset’s characteristics, distribution of variables, and identify potential correlations or patterns. Descriptive statistics, data visualization techniques, and correlation analysis are employed to gain insights into the dataset.
K-Nearest Neighbors (KNN)
The KNN algorithm is applied to the Heart Disease dataset to predict the presence of heart disease. The KNN model is trained using different values of k, and the accuracy, precision, recall, and F1-score metrics are evaluated. The optimal value of k is determined, and the performance of the KNN model is assessed.
Decision Trees
Decision Trees are employed to predict heart disease in patients. The decision tree model is constructed using the dataset, and its performance is evaluated using appropriate evaluation metrics such as accuracy, precision, recall, and F1-score. The decision tree is visualized to interpret the generated rules and understand the key features influencing the prediction.
Random Forest
The Random Forest algorithm, a popular ensemble method, is utilized to predict heart disease. A random forest model is trained using the Heart Disease dataset, and its performance is assessed. Feature importance analysis is conducted to identify the most significant predictors contributing to the prediction.
Results and Conclusion
Based on the evaluation of the KNN, Decision Trees, and Random Forest algorithms, conclusions are drawn regarding their predictive performance and suitability for predicting heart disease in patients. Each algorithm’s strengths and limitations are discussed, providing insights into their practical applicability. The findings of this research paper contribute to the understanding of machine learning techniques for heart disease prediction.
In conclusion, this research paper analyzes the Heart Disease dataset using KNN, Decision Trees, and Random Forest algorithms. The paper presents exploratory data analysis, the application of each algorithm, and evaluation of their predictive performance. The conclusions drawn from this analysis provide valuable insights into the effectiveness of these algorithms for heart disease prediction, aiding in the early detection and management of this critical health condition.
