Description

This heart disease dataset is acquired from one of the multispecialty hospitals in India. Over 14 common features make it one of the heart disease datasets available so far for research purposes. This dataset consists of 1000 subjects with 12 features. This dataset will be useful for building early-stage heart disease detection as well as for generating predictive machine-learning models.

Categories

Health Sciences

Acknowledgements & Source

Bhanu Prakash Doppala, Debnath Bhattacharyya

Institutions:Lincoln University College

Data Source

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Context

The Heart Attack Risk Prediction Dataset serves as a valuable resource for delving into the intricate dynamics of heart health and its predictors. Heart attacks, or myocardial infarctions, continue to be a significant global health issue, necessitating a deeper comprehension of their precursors and potential mitigating factors. This dataset encapsulates a diverse range of attributes including age, cholesterol levels, blood pressure, smoking habits, exercise patterns, dietary preferences, and more, aiming to elucidate the complex interplay of these variables in determining the likelihood of a heart attack. By employing predictive analytics and machine learning on this dataset, researchers and healthcare professionals can work towards proactive strategies for heart disease prevention and management. The dataset stands as a testament to collective efforts to enhance our understanding of cardiovascular health and pave the way for a healthier future.

Content

This synthetic dataset provides a comprehensive array of features relevant to heart health and lifestyle choices, encompassing patient-specific details such as age, gender, cholesterol levels, blood pressure, heart rate, and indicators like diabetes, family history, smoking habits, obesity, and alcohol consumption. Additionally, lifestyle factors like exercise hours, dietary habits, stress levels, and sedentary hours are included. Medical aspects comprising previous heart problems, medication usage, and triglyceride levels are considered. Socioeconomic aspects such as income and geographical attributes like country, continent, and hemisphere are incorporated. The dataset, consisting of 8763 records from patients around the globe, culminates in a crucial binary classification feature denoting the presence or absence of a heart attack risk, providing a comprehensive resource for predictive analysis and research in cardiovascular health.

Dataset Glossary (Column-wise)

• Patient ID - Unique identifier for each patient
• Age - Age of the patient
• Sex - Gender of the patient (Male/Female)
• Cholesterol - Cholesterol levels of the patient
• Blood Pressure - Blood pressure of the patient (systolic/diastolic)
• Heart Rate - Heart rate of the patient
• Diabetes - Whether the patient has diabetes (Yes/No)
• Family History - Family history of heart-related problems (1: Yes, 0: No)
• Smoking - Smoking status of the patient (1: Smoker, 0: Non-smoker)
• Obesity - Obesity status of the patient (1: Obese, 0: Not obese)
• Alcohol Consumption - Level of alcohol consumption by the patient (None/Light/Moderate/Heavy)
• Exercise Hours Per Week - Number of exercise hours per week
• Diet - Dietary habits of the patient (Healthy/Average/Unhealthy)
• Previous Heart Problems - Previous heart problems of the patient (1: Yes, 0: No)
• Medication Use - Medication usage by the patient (1: Yes, 0: No)
• Stress Level - Stress level reported by the patient (1-10)
• Sedentary Hours Per Day - Hours of sedentary activity per day
• Income - Income level of the patient
• BMI - Body Mass Index (BMI) of the patient
• Triglycerides - Triglyceride levels of the patient
• Physical Activity Days Per Week - Days of physical activity per week
• Sleep Hours Per Day - Hours of sleep per day
• Country - Country of the patient
• Continent - Continent where the patient resides
• Hemisphere - Hemisphere where the patient resides
• Heart Attack Risk - Presence of heart attack risk (1: Yes, 0: No)

Structure of the Dataset

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Acknowledgement

This dataset is a synthetic creation generated using ChatGPT to simulate a realistic experience. Its purpose is to provide a platform for beginners and data enthusiasts, allowing them to create, enjoy, practice, and learn from a dataset that mirrors real-world scenarios. The aim is to foster learning and experimentation in a simulated environment, encouraging a deeper understanding of data analysis and interpretation.

Cover Photo by: brgfx on Freepik

Thumbnail by: vectorjuice on Freepik

Cardiovascular diseases (CVDs) are the leading cause of death in India, with heart attacks (myocardial infarctions) accounting for a significant portion. India has a higher heart disease burden than many other nations, with cases occurring at younger ages compared to Western countries. This dataset incorporates key medical and lifestyle risk factors such as diabetes, hypertension, obesity, smoking, air pollution exposure, and healthcare access.

With a diverse representation across India's states, the dataset reflects the urban-rural disparity in healthcare, lifestyle patterns, and emergency response times. It can be used for predictive modeling, machine learning applications, epidemiological research, and policy analysis to improve early detection and intervention strategies for heart disease.

This heart disease dataset is curated by combining 3 popular heart disease datasets. The first dataset (Collected from Kaggle) contains 70000 records with 11 independent features which makes it the largest heart disease dataset available so far for research purposes. These data were collected at the moment of medical examination and information given by the patient. Second and third datasets contain 303 and 293 intstances respectively with 13 common features. The three datasets used for its curation are:Cardio Data (Kaggle Dataset)

Heart Disease Risk Prediction Dataset

Overview

This synthetic dataset is designed to predict the risk of heart disease based on a combination of symptoms, lifestyle factors, and medical history. Each row in the dataset represents a patient, with binary (Yes/No) indicators for symptoms and risk factors, along with a computed risk label indicating whether the patient is at high or low risk of developing heart disease.

The dataset contains 70,000 samples, making it suitable for training machine learning models for classification tasks. The goal is to provide researchers, data scientists, and healthcare professionals with a clean and structured dataset to explore predictive modeling for cardiovascular health.

This dataset is a side project of EarlyMed, developed by students of Vellore Institute of Technology (VIT-AP). EarlyMed aims to leverage data science and machine learning for early detection and prevention of chronic diseases.

Dataset Features

Input Features

Symptoms (Binary - Yes/No)

1. Chest Pain (chest_pain): Presence of chest pain, a common symptom of heart disease.
2. Shortness of Breath (shortness_of_breath): Difficulty breathing, often associated with heart conditions.
3. Unexplained Fatigue (fatigue): Persistent tiredness without an obvious cause.
4. Palpitations (palpitations): Irregular or rapid heartbeat.
5. Dizziness/Fainting (dizziness): Episodes of lightheadedness or fainting.
6. Swelling in Legs/Ankles (swelling): Swelling due to fluid retention, often linked to heart failure.
7. Pain in Arm/Jaw/Neck/Back (radiating_pain): Radiating pain, a hallmark of angina or heart attacks.
8. Cold Sweats & Nausea (cold_sweats): Symptoms commonly associated with acute cardiac events.

Risk Factors (Binary - Yes/No or Continuous)

1. Age (age): Patient's age in years (continuous variable).
2. High Blood Pressure (hypertension): History of hypertension (Yes/No).
3. High Cholesterol (cholesterol_high): Elevated cholesterol levels (Yes/No).
4. Diabetes (diabetes): Diagnosis of diabetes (Yes/No).
5. Smoking History (smoker): Whether the patient is a smoker (Yes/No).
6. Obesity (obesity): Obesity status (Yes/No).
7. Family History of Heart Disease (family_history): Family history of cardiovascular conditions (Yes/No).

Output Label

• Heart Disease Risk (risk_label): Binary label indicating the risk of heart disease:
  • 0: Low risk
  • 1: High risk

Data Generation Process

This dataset was synthetically generated using Python libraries such as numpy and pandas. The generation process ensured a balanced distribution of high-risk and low-risk cases while maintaining realistic correlations between features. For example: - Patients with multiple risk factors (e.g., smoking, hypertension, and diabetes) were more likely to be labeled as high risk. - Symptom patterns were modeled after clinical guidelines and research studies on heart disease.

Sources of Inspiration

The design of this dataset was inspired by the following resources:

Books

• "Harrison's Principles of Internal Medicine" by J. Larry Jameson et al.: A comprehensive resource on cardiovascular diseases and their symptoms.
• "Mayo Clinic Cardiology" by Joseph G. Murphy et al.: Provides insights into heart disease risk factors and diagnostic criteria.

Research Papers

• Framingham Heart Study: A landmark study identifying key risk factors for cardiovascular disease.
• American Heart Association (AHA) Guidelines: Recommendations for diagnosing and managing heart disease.

Existing Datasets

• UCI Heart Disease Dataset: A widely used dataset for heart disease prediction.
• Kaggle’s Heart Disease datasets: Various datasets contributed by the community.

Clinical Guidelines

• Centers for Disease Control and Prevention (CDC): Information on heart disease symptoms and risk factors.
• World Health Organization (WHO): Global statistics and risk factor analysis for cardiovascular diseases.

Applications

This dataset can be used for a variety of purposes:

1. Machine Learning Research:

   • Train classification models (e.g., Logistic Regression, Random Forest, XGBoost) to predict heart disease risk.
   • Experiment with feature engineering, model tuning, and evaluation metrics like Accuracy, Precision, Recall, and ROC-AUC.

2. Healthcare Analytics:

   • Identify key risk factors contributing to heart disease.
   • Develop decision support systems for early detection of cardiovascular risks.

3. Educational Purposes:

   • Teach students and practitioners about predictive modeling in healthcare.
   • Demonstrate the importance of feature selection...

This heart disease dataset is curated by combining 5 popular heart disease datasets already available independently but not combined before. In this dataset

The "Framingham" heart disease dataset includes over 4,240 records,16 columns and 15 attributes. The goal of the dataset is to predict whether the patient has 10-year risk of future (CHD) coronary heart disease

Adaptation of http://archive.ics.uci.edu/ml/datasets/Heart+Disease

Ready for usage with ehrapy

Description:

This dataset contains information related to individuals and their risk factors for heart disease. The data includes demographic information such as age and gender, as well as medical history, lifestyle factors, and symptoms associated with heart disease. The target variable indicates whether an individual has been diagnosed with heart disease or not.

Column Descriptions:

Age: Age of the individual (years). Gender: Gender of the individual (Male/Female). Cholesterol: Cholesterol level in mg/dL. Blood Pressure: Systolic blood pressure in mmHg. Heart Rate: Heart rate in beats per minute. Smoking: Smoking status (Never/Former/Current). Alcohol Intake: Alcohol intake frequency (None/Moderate/Heavy). Exercise Hours: Hours of exercise per week. Family History: Family history of heart disease (Yes/No). Diabetes: Diabetes status (Yes/No). Obesity: Obesity status (Yes/No). Stress Level: Stress level on a scale of 1 to 10. Blood Sugar: Fasting blood sugar level in mg/dL. Exercise Induced Angina: Presence of exercise-induced angina (Yes/No). Chest Pain Type: Type of chest pain experienced (Typical Angina/Atypical Angina/Non-anginal Pain/Asymptomatic). Heart Disease: Target variable indicating presence of heart disease (0: No, 1: Yes).

Exploring Risk Factors for Cardiovascular Disease in Adults

Examining Age, Gender, Height, Weight and Health Metrics

By Kuzak Dempsy [source]

About this dataset

This dataset contains detailed information on the risk factors for cardiovascular disease. It includes information on age, gender, height, weight, blood pressure values, cholesterol levels, glucose levels, smoking habits and alcohol consumption of over 70 thousand individuals. Additionally it outlines if the person is active or not and if he or she has any cardiovascular diseases. This dataset provides a great resource for researchers to apply modern machine learning techniques to explore the potential relations between risk factors and cardiovascular disease that can ultimately lead to improved understanding of this serious health issue and design better preventive measures

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How to use the dataset

This dataset can be used to explore the risk factors of cardiovascular disease in adults. The aim is to understand how certain demographic factors, health behaviors and biological markers affect the development of heart disease.

To start, look through the columns of data and familiarize yourself with each one. Understand what each field means and how it relates to heart health: - Age: Age of participant (integer) - Gender: Gender of participant (male/female). - Height: Height measured in centimeters (integer) - Weight: Weight measured in kilograms (integer) - Ap_hi: Systolic blood pressure reading taken from patient (integer) - Ap_lo : Diastolic blood pressure reading taken from patient (integer) - Cholesterol : Total cholesterol level read as mg/dl on a scale 0 - 5+ units( integer). Each unit denoting increase/decrease by 20 mg/dL respectively.
‐ Gluc : Glucose level read as mmol/l on a scale 0 - 16+ units( integer). Each unit denoting increase Decreaseby 1 mmol/L respectively. ‐ Smoke : Whether person smokes or not(binary; 0= No , 1=Yes). ‐ Alco ​ : Whether person drinks alcohol or not(binary; 0 =No ,1 =Yes ). • Active : whether person physically active or not( Binary ;0 =No,1 = Yes ). . Cardio : whether person suffers from cardiovascular diseases or not(Binary ;0 – no , 1 ‑yes ).Identify any trends between the different values for each attribute and the developmetn for cardiovascular disease among individuals represented by this dataset . Age, gender, weight, lifestyle practices like smoking & drinking alcohol are all key influences when analyzing this problem set. You can always modify pieces of your analysis until you're able to find patterns that will enable you make conclusions based on your understanding & exploration. You can further enrich your understanding using couple mopdeling technique like Regressions & Classification models over this dataset alongwith latest Deep Learning approach! Have Fun!

Research Ideas

• Analyzing the effect of lifestyle and environmental factors on the risk of cardiovascular disease.
• Predicting the risks of different age groups based on their demographic characteristics such as gender, height, weight and smoking status.
• Detecting patterns between levels of physical activity, blood pressure and cholesterol levels with likelihood of developing cardiovascular disease among individuals

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: heart_data.csv | Column name | Description | |:----------------|:---------------------------------------------------------| | age | Age of the individual. (Integer) | | gender | Gender of the individual. (String) | | height | Height of the individual in centimeters. (Integer) | | weight | Weight of the individual in kilograms. (Integer) | | ap_hi | Systolic blood pressure reading. (Integer) | | ap_lo | Diastolic blood pressure reading. (Integer) | | cholesterol | Cholesterol level of the individual. (Integer) | | gluc |...

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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

1. Age: age of the patient [years]
2. Sex: sex of the patient [M: Male, F: Female]
3. ChestPainType: chest pain type [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic]
4. RestingBP: resting blood pressure [mm Hg]
5. Cholesterol: serum cholesterol [mm/dl]
6. FastingBS: fasting blood sugar [1: if FastingBS > 120 mg/dl, 0: otherwise]
7. 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]
8. MaxHR: maximum heart rate achieved [Numeric value between 60 and 202]
9. ExerciseAngina: exercise-induced angina [Y: Yes, N: No]
10. Oldpeak: oldpeak = ST [Numeric value measured in depression]
11. ST_Slope: the slope of the peak exercise ST segment [Up: upsloping, Flat: flat, Down: downsloping]
12. 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:

1. Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D.
2. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.
3. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.
4. 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

Overview

This dataset contains medical data used for predicting heart disease. The data includes various attributes such as age, sex, chest pain type (cp), resting blood pressure (trestbps), cholesterol (chol), fasting blood sugar (fbs), resting electrocardiographic results (restecg), maximum heart rate achieved (thalach), exercise-induced angina (exang), and ST depression induced by exercise relative to rest (oldpeak).

Columns

age: Age of the patient (in years) sex: Sex of the patient (1 = male, 0 = female) cp: Chest pain type (1-4) trestbps: Resting blood pressure (in mm Hg on admission to the hospital) chol: Serum cholesterol in mg/dl fbs: Fasting blood sugar > 120 mg/dl (1 = true; 0 = false) restecg: Resting electrocardiographic results (0-2) thalach: Maximum heart rate achieved exang: Exercise-induced angina (1 = yes; 0 = no) oldpeak: ST depression induced by exercise relative to rest

Dataset

This dataset was created by Rithik Kotha

Contents

The Heart Attack Analysis & Prediction Dataset is a dataset used for research and analysis in the field of cardiovascular health. It typically contains various attributes related to an individual's health and lifestyle, along with an indicator of whether the individual experienced a heart attack or not.

Here are some common attributes found in such datasets:

Age: The age of the individual. **Sex: **The gender of the individual. Chest Pain Type: The type of chest pain experienced by the individual. Resting Blood Pressure: The resting blood pressure of the individual (in mm Hg). **Cholesterol: **The cholesterol levels of the individual (in mg/dL). **Fasting Blood Sugar: **The fasting blood sugar level of the individual (> 120 mg/dL is considered high). **Resting Electrocardiographic Results (ECG): **Results of the resting electrocardiogram. Maximum Heart Rate Achieved: The maximum heart rate achieved by the individual during exercise. **Exercise Induced Angina: **Whether the individual experienced angina during exercise. **ST Depression Induced by Exercise: **The ST depression induced by exercise relative to rest. **Slope of the Peak Exercise ST Segment: **The slope of the peak exercise ST segment. **Number of Major Vessels Colored by Fluoroscopy: **The number of major blood vessels colored by fluoroscopy. **Thalassemia: **A blood disorder; different types of thalassemia might be represented in the dataset. **Target: **Whether the individual had a heart attack or not (typically represented as binary: 0 for no heart attack, 1 for heart attack).

This dataset is often used for building machine learning models to predict the likelihood of a person having a heart attack based on their health attributes. Researchers and healthcare professionals use such models to better understand risk factors and develop preventive strategies for cardiovascular diseases.

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About this dataset Age : Age of the patient

Sex : Sex of the patient

exang: exercise induced angina (1 = yes; 0 = no)

ca: number of major vessels (0-3)

cp : Chest Pain type chest pain type

Value 1: typical angina Value 2: atypical angina Value 3: non-anginal pain Value 4: asymptomatic trtbps : resting blood pressure (in mm Hg)

chol : cholestoral in mg/dl fetched via BMI sensor

fbs : (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)

rest_ecg : resting electrocardiographic results

Value 0: normal Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV) Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria thalach : maximum heart rate achieved

target : 0= less chance of heart attack 1= more chance of heart attack

n

This dataset contains records related to the diagnosis of heart disease in patients. It includes various clinical and non-clinical attributes used to determine the presence or absence of heart disease. The data was collected from Kaggle, and it consists of 918 entries with 11 features. Columns cover aspects like patient demographics, vital signs, symptoms, and potential risk factors associated with heart conditions. Prior to analysis, preprocessing steps. This dataset can be utilized for exploratory data analysis, predictive modeling, and uncovering patterns contributing to heart disease diagnosis.

PLEASE UPVOTE Try some basic EDA on this dataset and try simpler model on this dataset and post your work.

Features:

Age | Objective Feature | age | int (days)

Height | Objective Feature | height | int (cm) |

Weight | Objective Feature | weight | float (kg) |

Gender | Objective Feature | gender | categorical code |

Systolic blood pressure | Examination Feature | ap_hi | int |

Diastolic blood pressure | Examination Feature | ap_lo | int |

Cholesterol | Examination Feature | cholesterol | 1: normal, 2: above normal, 3: well above normal |

Glucose | Examination Feature | gluc | 1: normal, 2: above normal, 3: well above normal |

Smoking | Subjective Feature | smoke | binary |

Alcohol intake | Subjective Feature | alco | binary |

Physical activity | Subjective Feature | active | binary |

Presence or absence of cardiovascular disease | Target Variable | cardio | binary |

Dataset

This dataset was created by Abel Tesfaye Sisaye

Contents

This dataset contains information related to cardiac health, with 1,025 data entries. Each entry represents a patient and includes the following attributes:

• Age: The age of the patient.
• Sex: The gender of the patient.
• Chest pain type: A categorical attribute indicating the type of chest pain experienced by the patient. It has four possible values.
• Resting blood pressure: The resting blood pressure of the patient.
• Serum cholestoral: The serum cholesterol level in mg/dl of the patient.
• Fasting blood sugar: Indicates whether the patient's fasting blood sugar is greater than 120 mg/dl.
• Resting electrocardiographic results: A categorical attribute representing the results of the resting electrocardiogram. It has three possible values.
• Maximum heart rate achieved: The maximum heart rate achieved by the patient.
• Exercise induced angina: Indicates whether the patient experienced angina (chest pain) induced by exercise.
• Oldpeak: ST depression induced by exercise relative to rest.
• Slope: The slope of the peak exercise ST segment.
• Number of major vessels: Represents the number of major blood vessels colored by fluoroscopy (ranging from 0 to 3).
• Thal: A categorical attribute indicating the thalassemia type of the patient. It has three possible values: 0 for normal, 1 for fixed defect, and 2 for reversible defect. Target: The target column serves as the outcome variable and indicates the presence of heart disease in the patient. A value of 0 signifies the absence of heart disease, while a value of 1 indicates the presence of heart disease.

The heart attack datasets were collected at Zheen hospital in Erbil, Iraq, from January 2019 to May 2019. The attributes of this dataset are: age, gender, heart rate, systolic blood pressure, diastolic blood pressure, blood sugar, ck-mb and troponin with negative or positive output.

Feature Engineering, EDA, and Prediction in this Notebook: EDA and Heart Attack Prediction | 99% Accuracy

According to the provided information, the medical dataset classifies either heart attack or none. The gender column in the data is normalized: the male is set to 1 and the female to 0. The glucose column is set to 1 if it is > 120; otherwise, 0. As for the output, positive is set to 1 and negative to 0.

The dataset has 9 column: Age: The patient's age

Gender: Biological sex of the patient (The male is set to 1 and the female to 0)

Heart Rate: The number of heartbeats per minute

Systolic Blood Pressure: The pressure in arteries when the heart contracts

Diastolic Blood Pressure: The pressure in arteries between heartbeats

Blood Sugar: The patient's blood glucose level

Ck-mb: A cardiac enzyme released during heart muscle damage

Troponin:A highly specific protein biomarker for heart muscle injury

Result: The outcome label indicating whether or not the patient experienced a heart attack

Institutions University of Kurdistan Hewler

Categories Medicine, Heart Disease

DOI: Rashid, Tarik A.; Hassan, Bryar (2022), “Heart Attack Dataset”, Mendeley Data, V1, doi: 10.17632/wmhctcrt5v.1

The Data set consists of 1200 records of Cardiovascular ECGs where each of the 300 records belongs to one ailment, in such a way 4 ailments have been considered. The original signals are taken from the MIT-BIH physio-net Database. One ailment is the MIT-BIH Arrhythmia Database, the other is BIDMC Congestive Heart Failure Database and MIT-BIH Atrial Fibrillation Database and finally MIT-BIH Normal Sinus Rhythm Database. From these four databases, ECG records have been segmented at 4120 samples each forming 300 signals. They are normalized with mentioned gain for each database and are preprocessed with bandpass filters. MODWPT technique was used to obtain 54 features that are given as columns in .csv file that is uploaded here. So the file has 1200 x 54 size records. Note:: Missing values have to be handled according to your application.

ACKNOWLEDGEMENT Please credit the authors if you use this dataset file in your research.

Citation:

1. Alekhya, L., and P. Rajesh Kumar, "A new approach to detect cardiovascular diseases using ECG scalograms and ML-based CNN algorithm." Mar 20, 2023. International Journal of Computational Vision and Robotics/Inderscience publishers. DOI: 10.1504/IJCVR.2022.10051429 Link: https://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=IJCVR

2. Alekhya, L., and P. Rajesh Kumar. "A Novel Application for Autonomous Detection of Cardiac Ailments using ECG Scalograms with Alex Net Convolution Neural Network." Design Engineering (2021): 13176-13189. Link: http://www.thedesignengineering.com/index.php/DE/article/view/6434

3. Autonomous Detection of Cardiac Ailments using Long-short term Memory Model based on Electrocardiogram signals, L. Alekhya, P. Rajesh Kumar, A. Venkata Sriram DOI: 10.14704/nq.2022.20.7.NQ33431. Pages: 3509 - 3518. Link: https://www.neuroquantology.com/open-access/Autonomous+Detection+of+Cardiac+Ailments+using+Longshort+term+Memory+Model+based+on+Electrocardiogram+signals_5781/

4. Autonomous Detection of Cardia Ailments diagnosed by Electrocardiogram using various Supervised Machine Learning AlgorithmsAutonomous Detection of Cardia Ailments diagnosed by Electrocardiogram using various Supervised Machine Learning Algorithms AMA, Agricultural Mechanization in Asia, Africa and Latin America (ISSN: 00845841) · Sep 18, 2021. Link: https://www.shin-norinco.com/article/autonomous-detection-of-cardia-ailments-diagnosed-by-electrocardiogram-using-various-supervised-machine-learning-algorithms

5. L Alekhya, P Rajesh Kumar, “Maximal Overlap Discrete Wavelet Packet Transform Based Characteristic waves detection in Electrocardiogram of Cardiovascular Diseases”, INTERNATIONAL JOURNAL OF SPECIAL EDUCATION, vol 36 (1), pp 51-61, 2021.

License License was not specified at the source, yet access to the data is public and a citation was requested.