Jun 1, 2022 · There are several machine and deep learning techniques available to classify the presence and absence of the disease. In this research, Logistic Regression (LR) techniques is applied to UCI dataset to classify the cardiac disease.

Mar 11, 2025 · In this article we will explore how Logistic regression to predict the likelihood of heart disease in patients. For this we will be importing Numpy, Pandas, Matplotlib, Seaborn, Statsmodels and sklearn library in python. statsmodels: for statistical modeling for fitting logistic regression. sklearn: Provides tools for machine learning modeling.

To develop a heart disease prediction model using logistic regression and identify significant factors. To compare the performance of logistic regression with prediction outcome from other classification model on detecting the heart diseases.

This research intends to pinpoint the most relevant/risk factors of heart disease as well as predict the overall risk using logistic regression. The dataset is publically available on the...

Visualizations and statistical summaries provided insights into the relationships between heart disease occurrence and key predictors. The final logistic regression model, validated by ROC curve and a confusion matrix, demonstrates a robust predictive capability.

Logistic Regression Model: Implementation of the logistic regression model for predicting the likelihood of a heart disease event. Model Training and Evaluation Script: A Python script that trains the model on the dataset and evaluates its accuracy and other metrics.

Feb 28, 2023 · Using the patient's various cardiac characteristics and the machine learning approach of logistic regression on a publicly accessible dataset from Kaggle, we developed and examined models for...

Traditional diagnostic methods for heart diseases are highly dependent on clinical expertise and can be time-consuming, making machine learning an attractive alternative for improving accuracy to diagnose and support faster decision making. ... This paper presents a logistic regression model to predict CVDs based on a dataset of 303 samples ...

In this paper the logistic regression algorithms is used and the health care data which classifies the patients whether they are having heart diseases or not according to the information in the record. Also I will try to use this data a model which predicts the patient whether they are having heart disease or not.

Using machine learning to predict heart disease could help doctors reduce risk. This study aims to analyze various aspects of patient data to provide accurate predictions of heart disease.