In this research paper it will be conducted and experimentally analysed to seek an improved method to predict heart disease in the upcoming years. So efficient steps can be taken in order to predict and treat the avoidable fatal heart problem. This work will be creating an efficient algorithm which will detect the disease on the basis of some parameters and give as much accurate information as possible. By using this method one can systematically predict the risk of suffering from this disease. The main feature utilized in the detection will include age, gender, max heart rate, exercise induced angina etc.
In today’s world the heart disease is increasing. Hence a lot of data related to the heart disease is being collected by using data mining. This important can be evaluated and used to predict and detect the coronary artery disease and heart related problem before the occurrence of the fatal experience. Many different types of life threating diseases are amongst people but heart disease has been studied the most in medical research. Early diagnosis of the disease is a very difficult task. We want to introduce an automated way of prediction of heart disease in individuals. This solution is not one and all solution but it will serve as a complementary diagnosis in the field of medical research. The main task in heart disease is to detect the disease early and treat it efficiently before any fatal experience occurs.
Table of Contents
1. Introduction
2. Heart disease
3. Literature review
4. Proposed Algorithm
4.1 Classification
4.2 Data set
4.3 Heart UCI Dataset
5. Used Prediction models
5.1 Naive Bayes
5.2 K nearest neighbor
5.3 Decision tree
5.4 Random forest
6. Experimentation Results
7. Conclusion
Research Objectives and Focus
The primary objective of this research is to develop an efficient, automated heart disease prediction system that leverages machine learning and data mining techniques to assist medical professionals in the early detection of coronary artery disease. By analyzing historical patient data, the system aims to identify risks before fatal occurrences, thereby providing a low-cost, supportive diagnostic tool.
- Application of supervised machine learning algorithms for disease classification.
- Preprocessing and feature extraction from the Cleveland heart disease dataset.
- Comparative analysis of prediction accuracy using various classification models.
- Identification of critical health parameters such as age, cholesterol, and exercise-induced angina.
Excerpt from the Book
Proposed Algorithm
In our system the main task is to predict how many people will get heart disease or not on the basis of the historical data available. Our architecture will work on a top to bottom approach. Heart disease dataset will be downloaded. Than the missing values from the dataset will be extracted. After dataset preprocessing the dataset will be divided into 0 and 1 format. When no heart disease will be detected the results will display 0 and 1 will be displayed for positivity of the heart disease.
Summary of Chapters
Introduction: Discusses the motivation for automated medical diagnosis and the prevalence of heart disease, highlighting the need for cost-effective, time-sensitive prediction systems.
Heart disease: Outlines the physiological role of the heart and identifies key biological and lifestyle risk factors contributing to heart conditions.
Literature review: Surveys existing research on medical data mining and summarizes various machine learning methodologies previously applied to cardiovascular disease prediction.
Proposed Algorithm: Details the systematic pipeline from data ingestion and preprocessing to the classification approach used to identify disease status.
Used Prediction models: Explains the theoretical foundations of the Naive Bayes, K-Nearest Neighbor, Decision Tree, and Random Forest algorithms as applied in this study.
Experimentation Results: Presents a quantitative performance comparison of the implemented models, specifically focusing on their prediction accuracy percentages.
Conclusion: Synthesizes the findings of the study, identifying the Random Forest algorithm as the most effective model for heart disease prediction among those tested.
Keywords
Machine learning, data mining, supervised learning, heart disease, predictive analysis, Cleveland dataset, classification, healthcare technology, feature extraction, diagnostic support, algorithm, accuracy, clinical decision support.
Frequently Asked Questions
What is the core focus of this research?
The research focuses on utilizing machine learning algorithms to automate the prediction of heart disease using historical patient records to enable earlier and more accurate medical intervention.
Which machine learning models were evaluated?
The study implemented and compared four specific supervised learning models: Naive Bayes, K-Nearest Neighbor (KNN), Decision Tree, and Random Forest.
What is the ultimate goal of the system?
The goal is to provide a reliable, supportive diagnostic tool that can help medical professionals detect cardiovascular risks in patients efficiently and accurately.
Which dataset serves as the basis for this study?
The research relies on the well-known Cleveland Heart Disease dataset, which contains specific attributes essential for systematic diagnosis.
How is the data processed before analysis?
The data undergoes rigorous preprocessing, including handling missing values, data dimensionality reduction, and feature extraction using Principal Component Analysis (PCA).
What are the primary features used for prediction?
Key features include patient age, gender, resting blood pressure, serum cholesterol, fasting blood sugar, and exercise-induced angina, among others.
Which algorithm performed best in the experiments?
The experimental results indicated that the Random Forest algorithm provided the highest accuracy at 94%, making it the optimal solution among those tested.
How does the system represent the final prediction results?
The system uses a binary classification format: 0 represents patients not having heart disease, and 1 represents patients testing positive for heart disease.
- Citar trabajo
- Daniyal Baig (Autor), 2020, Making heart diseases detectable. The invention of an algorithm for systematically predictions, Múnich, GRIN Verlag, https://www.grin.com/document/953437
