简介:本研究旨在对UCI数据库中的Heart_Disease_Dataset数据集进行深入的数据分析,并利用机器学习方法进行二分类预测。该数据集包含了心脏疾病相关的各种特征,如心电图、血压、心率等,是进行心脏疾病分析的重要资源。通过这些数据,我们能够更好地理解心脏疾病的模式,并为未来的诊断和治疗提供有力的支持。 背景:心脏疾病...
the patient is either healthy or sick. Therefore, heart disease prediction in this pipeline is a classification problem. The dataset used in this pipeline contains 14 feature fields and one goal field. During data preprocessing, the values of each field must be converted to numbers based on the...
However, in order to discover the optimal HDP solution, this study compares multiple classification algorithms utilizing two separate heart disease datasets from the Kaggle repository and the University of California, Irvine (UCI) machine learning repository. In a comparative analysis, Mean Absolute ...
In this paper, we proposed a novel machine learning model for heart disease prediction. The proposed method was tested on two different datasets from Kaggle and UCI. We applied sampling techniques to the unbalanced dataset and feature selection techniques are used to find the best features. Later...
Data Mining techniques can be used for disease prediction. In this research, the classification based data mining techniques are applied to healthcare data. This research focuses on the prediction of heart disease using three classification techniques namely Decision Trees, Naïve Bayes and K Nearest...
Performance evaluation of different machine learning techniques for prediction of heart disease. Neural Comput & Applic. 2018;29(10):685–93. Article Google Scholar Dua D, Graff C. UCI machine learning repository. School of Information and Computer Science, University of California, Irvine, CA. ...
The present study examines the role of feature selection methods in optimizing machine learning algorithms for predicting heart disease. The Cleveland Heart disease dataset with sixteen feature selection techniques in three categories of filter, wrapper,
1 佩 佩琪索菲亚 1枚 CC0 计算机视觉 2 22 2024-01-12 详情 相关项目 评论(0) 创建项目 文件列表 heart_disease_uci.csv heart_disease_uci.csv (0.08M) 下载反馈建议功能升级啦! •预置高频标签帮你快速锁定问题 •在线交流、邮件、电话,随你选择Hidden...
This study uses the Heart Disease Prediction dataset [13] which is located in the UCI standard data repository. The proposed approach first extracts important features in the dataset that are more correlated with the class label of instructional samples. Attribute correlation with a class label refer...
For heart disease prediction, considerable characteristics and data mining technique are recognized [11]. The heart disease datasets is taken from the data repository, UCI machine learning repository which is maintained by University of California Irvine. Z-Alizadeh dataset has been chosen since it is...