Heart disease prediction using retina images leverages advanced imaging and machine learning techniques to assess cardiovascular risk. The retina, being a highly vascularized structure, provides crucial insights into the condition of blood vessels, which are often affected in heart disease.

Heart disease prediction using retina images leverages advanced imaging and machine learning techniques to assess cardiovascular risk. The retina, being a highly vascularized structure, provides crucial insights into the condition of blood vessels, which are often affected in heart disease

心血管疾病已成为全球范围内致人死亡的头号病因。为了能有效预防心血管疾病，血压的连续测量尤为重要。目前，连续血压测量分为无创测量和有创测量两种方式。有创测量虽然能达到较高的精度，但是操作复杂且存在感染风险。无创测量主要基于脉搏波。随着机器学习的发展，愈来愈多的人使用脉搏波特征参数法。该方法主要存在两点问题。其一，手动提取特征对波形的要求较高，特征选取受研究者先验知识影响，极有可能提取到非相关特征。其二，血压波形中包含的丰富生理信息未能被充分挖掘。大多数研究的预测目标为收缩压、舒张压等单一血压值，较少的研究关注血压整体波形的预测。 针对上述问题，本文创新性地将原本用于二维图像处理的U-Net模型引入一维血压预测中，提出了一种基于U-Net的PPG-ABP转换模型。该方法无需手动提取特征，仅使用光电血管容积脉搏波（PPG）信号便可预测出连续血压波形。相较于脉搏波特征法，本文方法在信号获取和处理上更为便捷，在结果输出上包含更丰富的血压波形信息。本文平均血压预测结果满足美国医疗仪器促进协会（AAMI）标准。在英国高血压协会（BHS） 标准下，舒张压与平均血压可达到等级B。此外，本模型针对高血压与正常人群的血压分类也能取得较好的效果。Cardiovascular disease has become the significant cause of death. To prevent such disease effectively, continuous measurement of blood pressure is important. Nowadays, there are two ways of blood pressure measurement: noninvasive measurement and invasive measurement. Although invasive measurement can achieve high precision, it is complex to operate and has infection risk. The noninvasive measurement uses pulse waves. With the development of machine learning, many studies make handcrafted features from pulse waves to predict blood pressure. There are two problems with this method. Firstly, feature extraction requires a high standard for waveform, which is not easily achieved in reality. Besides, feature selection is influenced by prior knowledge of researchers. It is very likely to extract non-related features. Secondly, the abundant physiological information of the blood pressure waveform is not extracted fully. Specifically, most of the research aims to predict systolic pressure (SBP) and diastolic pressure (DBP). Indeed, less research focuses on the prediction of the overall waveform of blood pressure. Given the above problems, the thesis introduces the U-Net model, originally used in two-dimensional image processing, into one-dimensional blood pressure prediction. A model based on U-Net was proposed, directly converting photoplethysmogram (PPG) to arterial blood pressure (ABP). The method does not need to extract the features manually. The continuous blood pressure waveform can be predicted only by using the PPG signal. In term of signal acquisition and processing, this method is more convenient. What’s more, it contains more information of blood pressure waveform in the output. The results of the mean arterial pressure (MAP) prediction meet the AAMI standard. DBP and MAP can reach level B under the BHS standards. In addition, the model can also achieve ideal results in the classification of hypertension and normal people.

[MICCAI 2025] VAMPIRE: Uncovering Vessel Directional and Morphological Information from OCTA Images for Cardiovascular Disease Risk Factor Prediction

LOGISTIC REGRESSION - HEART DISEASE PREDICTION Introduction World Health Organization has estimated 12 million deaths occur worldwide, every year due to Heart diseases. Half the deaths in the United States and other developed countries are due to cardio vascular diseases. The early prognosis of cardiovascular diseases can aid in making decisions on lifestyle changes in high risk patients and in turn reduce the complications. This research intends to pinpoint the most relevant/risk factors of heart disease as well as predict the overall risk using logistic regression Data Preparation Source The dataset is publically available on the Kaggle website, and it is from an ongoing cardiovascular study on residents of the town of Framingham, Massachusetts. The classification goal is to predict whether the patient has 10-year risk of future coronary heart disease (CHD).The dataset provides the patients’ information. It includes over 4,000 records and 15 attributes. Variables Each attribute is a potential risk factor. There are both demographic, behavioral and medical risk factors. Demographic: • Sex: male or female(Nominal) • Age: Age of the patient;(Continuous - Although the recorded ages have been truncated to whole numbers, the concept of age is continuous) Behavioral • Current Smoker: whether or not the patient is a current smoker (Nominal) • Cigs Per Day: the number of cigarettes that the person smoked on average in one day.(can be considered continuous as one can have any number of cigarettes, even half a cigarette.) Medical( history) • BP Meds: whether or not the patient was on blood pressure medication (Nominal) • Prevalent Stroke: whether or not the patient had previously had a stroke (Nominal) • Prevalent Hyp: whether or not the patient was hypertensive (Nominal) • Diabetes: whether or not the patient had diabetes (Nominal) Medical(current) • Tot Chol: total cholesterol level (Continuous) • Sys BP: systolic blood pressure (Continuous) • Dia BP: diastolic blood pressure (Continuous) • BMI: Body Mass Index (Continuous) • Heart Rate: heart rate (Continuous - In medical research, variables such as heart rate though in fact discrete, yet are considered continuous because of large number of possible values.) • Glucose: glucose level (Continuous) Predict variable (desired target) • 10 year risk of coronary heart disease CHD (binary: “1”, means “Yes”, “0” means “No”) Logistic Regression Logistic regression is a type of regression analysis in statistics used for prediction of outcome of a categorical dependent variable from a set of predictor or independent variables. In logistic regression the dependent variable is always binary. Logistic regression is mainly used to for prediction and also calculating the probability of success. The results above show some of the attributes with P value higher than the preferred alpha(5%) and thereby showing low statistically significant relationship with the probability of heart disease. Backward elimination approach is used here to remove those attributes with highest P-value one at a time followed by running the regression repeatedly until all attributes have P Values less than 0.05. Feature Selection: Backward elimination (P-value approach) Logistic regression equation P=eβ0+β1X1/1+eβ0+β1X1P=eβ0+β1X1/1+eβ0+β1X1 When all features plugged in: logit(p)=log(p/(1−p))=β0+β1∗Sexmale+β2∗age+β3∗cigsPerDay+β4∗totChol+β5∗sysBP+β6∗glucoselogit(p)=log(p/(1−p))=β0+β1∗Sexmale+β2∗age+β3∗cigsPerDay+β4∗totChol+β5∗sysBP+β6∗glucose Interpreting the results: Odds Ratio, Confidence Intervals and P-values • This fitted model shows that, holding all other features constant, the odds of getting diagnosed with heart disease for males (sex_male = 1)over that of females (sex_male = 0) is exp(0.5815) = 1.788687. In terms of percent change, we can say that the odds for males are 78.8% higher than the odds for females. • The coefficient for age says that, holding all others constant, we will see 7% increase in the odds of getting diagnosed with CDH for a one year increase in age since exp(0.0655) = 1.067644. • Similarly , with every extra cigarette one smokes thers is a 2% increase in the odds of CDH. • For Total cholesterol level and glucose level there is no significant change. • There is a 1.7% increase in odds for every unit increase in systolic Blood Pressure. Model Evaluation - Statistics From the above statistics it is clear that the model is highly specific than sensitive. The negative values are predicted more accurately than the positives. Predicted probabilities of 0 (No Coronary Heart Disease) and 1 ( Coronary Heart Disease: Yes) for the test data with a default classification threshold of 0.5 lower the threshold Since the model is predicting Heart disease too many type II errors is not advisable. A False Negative ( ignoring the probability of disease when there actually is one) is more dangerous than a False Positive in this case. Hence in order to increase the sensitivity, threshold can be lowered. Conclusions • All attributes selected after the elimination process show P-values lower than 5% and thereby suggesting significant role in the Heart disease prediction. • Men seem to be more susceptible to heart disease than women. Increase in age, number of cigarettes smoked per day and systolic Blood Pressure also show increasing odds of having heart disease • Total cholesterol shows no significant change in the odds of CHD. This could be due to the presence of 'good cholesterol(HDL) in the total cholesterol reading. Glucose too causes a very negligible change in odds (0.2%) • The model predicted with 0.88 accuracy. The model is more specific than sensitive. Overall model could be improved with more data

Introduction In my case studies I keep writing in English because it is used in Kaggle and I also keep them in Portuguese because my native language is Brazilian Portuguese, so we can share more knowledge and experiences in Kaggle with our Brazilian colleagues. We will develop and analyze the algorithms with the best capacity and identify the problems in the heart and at the end we will make a comparison between them. Description Context Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worldwide. Four out of 5CVD deaths are due to heart attacks and strokes, and one-third of these deaths occur prematurely in people under 70 years of age. Heart failure is a common event caused by CVDs and this dataset contains 11 features that can be used to predict a possible heart disease. People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning model can be of great help. Attribute Information Age: age of the patient [years] Sex: sex of the patient [M: Male, F: Female] ChestPainType: chest pain type [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic] RestingBP: resting blood pressure [mm Hg] Cholesterol: serum cholesterol [mm/dl] FastingBS: fasting blood sugar [1: if FastingBS > 120 mg/dl, 0: otherwise] RestingECG: resting electrocardiogram results [Normal: Normal, ST: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV), LVH: showing probable or definite left ventricular hypertrophy by Estes' criteria] MaxHR: maximum heart rate achieved [Numeric value between 60 and 202] ExerciseAngina: exercise-induced angina [Y: Yes, N: No] Oldpeak: oldpeak = ST [Numeric value measured in depression] ST_Slope: the slope of the peak exercise ST segment [Up: upsloping, Flat: flat, Down: downsloping] HeartDisease: output class [1: heart disease, 0: Normal] Source This dataset was created by combining different datasets already available independently but not combined before. In this dataset, 5 heart datasets are combined over 11 common features which makes it the largest heart disease dataset available so far for research purposes. The five datasets used for its curation are: Cleveland: 303 observations Hungarian: 294 observations Switzerland: 123 observations Long Beach VA: 200 observations Stalog (Heart) Data Set: 270 observations Total: 1190 observations Duplicated: 272 observations Final dataset: 918 observations Every dataset used can be found under the Index of heart disease datasets from UCI Machine Learning Repository on the following link: https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/ Citation fedesoriano. (September 2021). Heart Failure Prediction Dataset. Retrieved [Date Retrieved] from https://www.kaggle.com/fedesoriano/heart-failure-prediction. Acknowledgements Creators: Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D. Donor: David W. Aha (aha '@' ics.uci.edu) (714) 856-8779

The prediction of cardiovascular disease has great significance for its prevention work. The clinical data information for different patients is difficult to be reasonably represented, also the time interval between events is different, which leads to problems such as difficult prediction. This paper proposes several algorithms based on electronic medical record data to study the risk prediction of cardiovascular disease. The model uses techniques such as neural network to characterize and learn the patient's historical electronic medical record data, which can not only effectively capture the time-series features of the electronic medical record data, but also consider the potential correlation between them, and ultimately improve cardiovascular disease risk prediction performance. The key result is that coronary artery related disease has a prominent influence on the prediction of cardiovascular disease risk.

This repository contains a Streamlit web application for analyzing heart disease data using exploratory data analysis and machine learning. It provides interactive visualizations and an AI-based prediction system using XGBoost to assess cardiovascular risk.

Machine Learning is the fastest-growing technique in many fields and the healthcare industry is no exception to this. Machine Learning algorithms plays an essential role in predicting the presence/absence of Heart diseases, tumors, and more. Such required information, if predicted well in advance, can provide important insights to doctors who can then adapt their diagnosis and treat the patient accordingly. World Health Organization has estimated 12 million deaths occur worldwide, every year due to heart diseases. Half the deaths in the United States and other developed countries are due to cardiovascular diseases. The early prognosis of stroke diseases can aid in making decisions on lifestyle changes in high-risk patients and in turn reduce the complications. If it is about to identify the relationship and factors affecting it can cured n advance time. This research intends to pinpoint the most relevant/risk factors of heart disease as well as predict the overall risk using logistic regression. In this report, I'll discuss the prediction of stroke using Machine Learning algorithms. The algorithm I have implemented is logistic regression on the Health

Cardiovascular disease risk prediction project leveraging Python, pandas, scikit-learn for modeling, and FastAPI for API deployment.

This project endeavors to develop an effective model for predicting the risk of heart attacks based on a thorough analysis of relevant factors. The insights gained from this analysis will contribute to a better understanding of cardiovascular health and risk factors.

Cardiovascular Disease Risk Prediction Tool based on UK Biobank

Here is a model for CVD Risk Prediction (Cardiovascular Disease Risk Prediction) with a Advanced Web app and aprox 1 Accuracy.

A ML based cardiovascular risk prediction system that uses an ensemble of XGBoost and CatBoost to provide real time heart disease risk estimates. With interactive health visualization and clean UI.

Real-Time Cardiovascular Disease Prediction (CVD-PRED) using PPG-to-ECG conversion with GANs (CardioGAN) and deep learning-based classifiers deployed with ESP32 IoT hardware. Aims low-cost, portable, and continuous cardiac monitoring for arrhythmia and CVD risk detection.

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Development of a Supervised Machine Learning Model for Cardiovascular Disease Risk Prediction Using Structured Health Data

Code for paper: Machine Learning and Atherosclerotic Cardiovascular Disease Risk Prediction in a Multi-Ethnic Population , Ward et al, NPJ 2020

Machine learning project for cardiovascular risk prediction. The goal is to predict whether the patient has a 10-year risk of future coronary heart disease (CHD)

AI-powered tool combining retinal disease detection and heart disease risk prediction, enhanced with Gemini LLM insights. Built using Python and Flask, it leverages RFMiD and CDC BRFSS datasets to provide an interpretable cardiovascular risk assessment from fundus images and user input.

Arrhythmia is the leading cardiovascular disease in the world.In this deep neural networks are applied to identify cardiac health risk and estimate severity by learning pattern within the database which serve as the basic predictions about patient heart condition

Nowadays, Smartphone with various extraordinary and notable sensors makes new invigorating open entryways for Data Mining and Machine Learning; other than makes another exploration field for Human Activity Recognition a.k.a. HAR. The HAR system has been applied to many areas, such as, step counting, health monitoring, abnormal activity recognition and so forth. Diagnosis of diseases is one of the unvisited applications which are up 'til now neglected application domains for HAR. It has been found that, physical activity is endorsed by physicians to patients suffering from Non-Insulin Dependent Diabetes Mellitus a.k.a. NIDDM, owing to the fact that it enhances susceptibility of insulin. Thus, the HAR system may allow the early diagnosis issue of Diabetes. That being so, activities entitled as walking, walking upstairs, walking downstairs, jogging and cycling which are associated to cardiovascular endurance, activities named drinking, eating, using toilet in order to record the log regarding of urination, activities such as fallen down facilitates the identification of physical weakness and activities such as genital itching, sitting, standing and lying enhances the facilitation regarding the identification of symptoms associated with diabetes type-2 or NIDDM, were subject matter to recognize. In the process of our work, we gathered sensors data from 10 subjects with an Android application, which was developed by us, to facilitate the Long Short-Term Memory a.k.a. LSTM to recognize entitled activities along with irrelevant activities to ensure the robustness of our system and we achieved utmost 98.4818% accuracy on test dataset. To know about how much time a diabetic patient spends in performing entitled symptomatic activities, we went to Chattogram Diabetic General Hospital and collected data from 97 diabetic patients. After collecting data from diabetic patients, we developed another Android application to collect sensors’ data from experimental subject in continuous manner. Thus, the data being gathered from the experimental subject performed over the most recent thirty days, we processed these data into pre-trained LSTM model and recognized the activities by prediction. In this way, we figured the mean time spent in executing of every activity from our experimental subject’s predicted activity log. We utilized this mean duration of performing symptomatic activities to obtained similarity qualities characterizing the similarity of our experimental subject with the diabetic symptoms from 97 patients. The similarity measure of 57.3916199% put the experimental subject into the class of moderate risk factor. As far as similarity measure, we have seen that our procedure of discovering the risk factor really shown higher performance.

Prediction of Cardiovascular Disease using Machine Learning (ML)

This is basically my ongoing personal project based on ID3 based machine learning model, without using Scikit-Learn

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Machine learning project to predict cardiovascular disease risk

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