相关论文: Comparative Study of Machine Learning Algorithms i…
Cardiovascular disease remains a significant problem in modern society. Among non-invasive techniques, the electrocardiogram (ECG) is one of the most reliable methods for detecting abnormalities in cardiac activities. However, ECG…
Heart disease is the leading cause of death worldwide. Currently, 33% of cases are misdiagnosed, and approximately half of myocardial infarctions occur in people who are not predicted to be at risk. The use of Artificial Intelligence could…
Cardiovascular diseases are a pervasive global health concern, contributing significantly to morbidity and mortality rates worldwide. Among these conditions, arrhythmia, characterized by irregular heart rhythms, presents formidable…
Lung cancer is a major issue in worldwide public health, requiring early diagnosis using stable techniques. This work begins a thorough investigation of the use of machine learning (ML) methods for precise classification of lung cancer…
Machine Learning and Artificial Intelligence can be widely used to diagnose chronic diseases so that necessary precautionary treatment can be done in critical time. Diabetes Mellitus which is one of the major diseases can be easily…
Investigation on the electrocardiogram (ECG) signals is an essential way to diagnose heart disease since the ECG process is noninvasive and easy to use. This work presents a supraventricular arrhythmia prediction model consisting of a few…
Background: Cardiovascular diseases (CVDs) are the leading cause of death globally. The use of artificial intelligence (AI) methods - in particular, deep learning (DL) - has been on the rise lately for the analysis of different CVD-related…
Improving the precision of heart diseases detection has been investigated by many researchers in the literature. Such improvement induced by the overwhelming health care expenditures and erroneous diagnosis. As a result, various…
This project intends to study a cardiovascular disease risk early warning model based on one-dimensional convolutional neural networks. First, the missing values of 13 physiological and symptom indicators such as patient age, blood glucose,…
This paper explores machine learning (ML) models for classifying lung cancer levels to improve diagnostic accuracy and prognosis. Through parameter tuning and rigorous evaluation, we assess various ML algorithms. Techniques like minimum…
In primary diagnosis and analysis of heart defects, an ECG signal plays a significant role. This paper presents a model for the prediction of ventricular tachycardia arrhythmia using noise filtering, a unique set of ECG features, and a…
Metabolomic data can potentially enable accurate, non-invasive and low-cost prediction of coronary artery disease. Regression-based analytical approaches however might fail to fully account for interactions between metabolites, rely on a…
Cardiovascular disease is the number one cause of death all over the world. Data mining can help to retrieve valuable knowledge from available data from the health sector. It helps to train a model to predict patients' health which will be…
The global need for effective disease diagnosis remains substantial, given the complexities of various disease mechanisms and diverse patient symptoms. To tackle these challenges, researchers, physicians, and patients are turning to machine…
Heart disease is one of the significant challenges in today's world and one of the leading causes of many deaths worldwide. Recent advancement of machine learning (ML) application demonstrates that using electrocardiogram (ECG) and patient…
Automated classification of electrocardiogram (ECG) signals is a useful tool for diagnosing and monitoring cardiovascular diseases. This study compares three traditional machine learning algorithms (Decision Tree Classifier, Random Forest…
The use of machine learning algorithms in healthcare can amplify social injustices and health inequities. While the exacerbation of biases can occur and compound during the problem selection, data collection, and outcome definition, this…
Nowadays, Breast cancer has risen to become one of the most prominent causes of death in recent years. Among all malignancies, this is the most frequent and the major cause of death for women globally. Manually diagnosing this disease…
Today data mining techniques are exploited in medical science for diagnosing, overcoming and treating diseases. Neural network is one of the techniques which are widely used for diagnosis in medical field. In this article efficiency of nine…
The rapid advancements in Artificial Intelligence, specifically Machine Learning (ML) and Deep Learning (DL), have opened new prospects in medical sciences for improved diagnosis, prognosis, and treatment of severe health conditions. This…