Explore our synthetic healthcare dataset designed for machine learning, data science, and healthcare analytics.

Heterogenous Big dataset is presented in this proposed work: electrocardiogram (ECG) signal, blood pressure signal, oxygen saturation (SpO2) signal, and the text input. This work is an extension version for our relevant formulating of dataset that presented in [1] and a trustworthy and relevant medical dataset library (PhysioNet [2]) was used to acquire these signals. The dataset includes medical features from heterogenous sources (sensory data and non-sensory). Firstly, ECG sensor’s signals which contains QRS width, ST elevation, peak numbers, and cycle interval. Secondly: SpO2 level from SpO2 sensor’s signals. Third, blood pressure sensors’ signals which contain high (systolic) and low (diastolic) values and finally text input which consider non-sensory data. The text inputs were formulated based on doctors diagnosing procedures for heart chronic diseases. Python software environment was used, and the simulated big data is presented along with analyses.

Context

Part of Janatahack Hackathon in Analytics Vidhya

Content

The healthcare sector has long been an early adopter of and benefited greatly from technological advances. These days, machine learning plays a key role in many health-related realms, including the development of new medical procedures, the handling of patient data, health camps and records, and the treatment of chronic diseases.

MedCamp organizes health camps in several cities with low work life balance. They reach out to working people and ask them to register for these health camps. For those who attend, MedCamp provides them facility to undergo health checks or increase awareness by visiting various stalls (depending on the format of camp).

MedCamp has conducted 65 such events over a period of 4 years and they see a high drop off between “Registration” and number of people taking tests at the Camps. In last 4 years, they have stored data of ~110,000 registrations they have done.

One of the huge costs in arranging these camps is the amount of inventory you need to carry. If you carry more than required inventory, you incur unnecessarily high costs. On the other hand, if you carry less than required inventory for conducting these medical checks, people end up having bad experience.

The Process:

MedCamp employees / volunteers reach out to people and drive registrations.
During the camp, People who “ShowUp” either undergo the medical tests or visit stalls depending on the format of health camp.

Other things to note:

Since this is a completely voluntary activity for the working professionals, MedCamp usually has little profile information about these people.
For a few camps, there was hardware failure, so some information about date and time of registration is lost.
MedCamp runs 3 formats of these camps. The first and second format provides people with an instantaneous health score. The third format provides  
information about several health issues through various awareness stalls.

Favorable outcome:

For the first 2 formats, a favourable outcome is defined as getting a health_score, while in the third format it is defined as visiting at least a stall.
You need to predict the chances (probability) of having a favourable outcome.

Train / Test split:

Camps started on or before 31st March 2006 are considered in Train
Test data is for all camps conducted on or after 1st April 2006.

Acknowledgements

Credits to AV

Inspiration

To share with the data science community to jump start their journey in Healthcare Analytics

Deep Learning in Healthcare Market Outlook

The global deep learning in healthcare market size was valued at approximately $2.8 billion in 2023 and is projected to reach around $13.7 billion by 2032, growing at a robust compound annual growth rate (CAGR) of 19.4% during the forecast period. The rapid integration of artificial intelligence (AI) and machine learning technologies in healthcare systems, alongside advancements in computational power and data availability, are significant growth drivers for the market.

One of the primary growth factors for the deep learning in healthcare market is the increasing demand for efficient and accurate diagnostic tools. Deep learning algorithms have demonstrated superior performance in interpreting medical images, detecting anomalies, and predicting outcomes compared to traditional methods. This has led to widespread adoption in medical imaging, significantly enhancing diagnostic precision and reducing the burden on healthcare professionals. The ever-increasing volume of healthcare data, coupled with the need for quick and accurate decision-making, further propels the market forward. By leveraging large datasets, deep learning can achieve a level of precision and speed unattainable by human capabilities alone.

Another significant driver is the growing emphasis on personalized medicine. Deep learning enables the analysis of complex biological data, aiding in the development of personalized treatment plans tailored to individual patient profiles. This shift towards precision medicine is transforming patient care, allowing for more effective treatment protocols and better patient outcomes. The pharmaceutical industry, in particular, is investing heavily in deep learning technologies to expedite drug discovery and development processes, thereby reducing time-to-market and costs associated with bringing new drugs to consumers.

The adoption of electronic health records (EHRs) and the integration of AI in healthcare administration are also crucial growth factors. Deep learning algorithms can process vast amounts of patient data stored in EHRs to identify patterns and predict disease outbreaks, optimize resource allocation, and enhance patient management. The demand for streamlined operations and improved patient care is driving healthcare providers to incorporate these advanced technologies. Furthermore, the ongoing advancements in computational power and the availability of high-quality healthcare datasets are crucial enablers for the application of deep learning technologies in various healthcare domains.

Computer Vision in Healthcare is revolutionizing the way medical professionals approach diagnostics and treatment planning. By leveraging advanced image processing algorithms, computer vision can analyze medical images with remarkable accuracy, identifying patterns and anomalies that might be missed by the human eye. This technology is not only enhancing the precision of medical imaging but also enabling the development of automated systems that assist radiologists in interpreting complex datasets. The integration of computer vision in healthcare is streamlining workflows, reducing diagnostic errors, and ultimately improving patient outcomes. As the technology continues to evolve, its applications are expanding beyond imaging to include areas such as surgery, pathology, and patient monitoring, offering a comprehensive toolset for modern healthcare delivery.

On the regional front, North America holds the largest share of the deep learning in healthcare market, driven by substantial investments in AI technology, well-established healthcare infrastructure, and supportive government initiatives. The region's focus on technological innovation and its robust research ecosystem are key factors contributing to market growth. Moreover, the presence of leading AI and healthcare companies in North America accelerates the adoption of deep learning technologies. Europe and Asia Pacific are also witnessing significant growth, with the latter expected to exhibit the highest CAGR during the forecast period due to increasing healthcare digitization and rising investments in AI-driven healthcare solutions.

Component Analysis

The deep learning in healthcare market is segmented by component into software, hardware, and services. The software segment is anticipated to dominate the market owing to continuous advancements in AI algorithms and the development of sophisticated software solutions tailored for healthcar

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The MedVidCL dataset contains a collection of 6, 617 videos annotated into ‘medical instructional’, ‘medical non-instructional' and ‘non-medical’ classes. A two-step approach is used to construct the MedVidCL dataset. In the first step, the videos annotated by health informatics experts are used to train a machine learning model that predicts the given video to one of the three aforementioned classes. In the second step, only the high-confidence videos are used and health informatics experts assess the model’s predicted video category and update the category wherever needed.

Oral diseases affect nearly 3.5 billion people, with the majority residing in low- and middle-income countries. Due to limited healthcare resources, many individuals are unable to access proper oral healthcare services. Image-based machine learning technology is one of the most promising approaches to improving oral healthcare services and reducing patient costs. Openly accessible datasets play a crucial role in facilitating the development of machine learning techniques. However, existing dental datasets have limitations such as a scarcity of Cone Beam Computed Tomography (CBCT) data, lack of matched multi-modal data, and insufficient complexity and diversity of the data. This project addresses these challenges by providing a dataset that includes 329 CBCT images from 169 patients, multi-modal data with matching modalities, and images representing various oral health conditions.

This repository contains two healthcare datasets in Hindi and Punjabi, translated from English. The datasets cover medical diagnoses, disease names, and related healthcare information. The data has been carefully cleaned and formatted to ensure accuracy and usability for various applications, including machine learning, NLP, and healthcare analysis.

The purpose of these datasets is to facilitate research and development in regional language processing, especially in the healthcare sector.

FileMarket offers premium Machine Learning (ML) Data tailored for gesture recognition and various AI applications. Our globally sourced datasets are meticulously curated to ensure high quality and accuracy, providing a solid foundation for training robust and reliable ML models. In addition to ML data, we also specialize in Object Detection Data, Medical Imaging Data, Large Language Model (LLM) Data, and Deep Learning (DL) Data. Each category is crafted with precision to meet the diverse needs of cutting-edge AI and machine learning projects.

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Gesture recognition Computer vision Natural language processing (NLP) Predictive analysis Autonomous systems Why work with our data:

Global coverage: Our datasets are sourced from a worldwide network, ensuring diversity and inclusiveness. Scalability: We offer scalable solutions that grow with your project. Customization: Datasets can be tailored to fit your specific requirements, whether it’s for ML, object detection, medical imaging, or any other AI application. Enhance model performance: High-quality data that boosts the reliability and accuracy of your models. Versatility: Our datasets are applicable across various domains, from healthcare to autonomous systems. Empower your AI projects with FileMarket’s top-tier Machine Learning (ML) Data, Object Detection Data, Medical Imaging Data, Large Language Model (LLM) Data, and Deep Learning (DL) Data.

1) Data Introduction • The Healthcare Dataset is a synthetic dataset designed to mimic real-world healthcare data for data science, machine learning, and data analysis purposes. It includes patient information, medical conditions, admission details, and healthcare services provided. This dataset is ideal for developing and testing healthcare predictive models, practicing data manipulation techniques, and creating data visualizations.

2) Data Utilization (1) Healthcare data has characteristics that: • It includes detailed patient information such as age, gender, blood type, medical condition, and admission details. This information can be used to analyze healthcare trends, patient demographics, and the effectiveness of medical treatments. (2) Healthcare data can be used to: • Predictive Modeling: Helps in developing models to predict patient outcomes, treatment success rates, and disease progression. • Healthcare Analytics: Assists in analyzing patient data to identify patterns, improve patient care, and optimize resource allocation. • Educational Purposes: Supports learning and teaching data science concepts in a healthcare context, providing realistic data for experimentation and practice.

These synthetic patient datasets were created for machine learning (ML) study of lung cancer risk prediction in simulation of ML-enabled learning health systems. Five populations of 30K patients were generated by the Synthea patient generator. They were combined sequentially to form 5 different size populations, from 30K to 150K patients. Patients with or without lung cancer were selected roughly at 1:3 ratio and their electronic health records (EHR) were processed to data table files ready for machine learning. The ML-ready table files also have the continuous numeric values converted to categorical values. Because Synthea patients are closely resemble to real patients, these ML-ready dataset can be used to develop and test ML algorithms, and train researchers. Unlike real patient data, these Synthea datasets can be shared with collaborators anywhere without privacy concerns. The first use of these datasets was in a LHS simulation study, which was published in Nature Scientific Reports (see https://www.nature.com/articles/s41598-022-23011-4).

Dataset Description:

This dataset comprises transcriptions of conversations between doctors and patients, providing valuable insights into the dynamics of medical consultations. It includes a wide range of interactions, covering various medical conditions, patient concerns, and treatment discussions. The data is structured to capture both the questions and concerns raised by patients, as well as the medical advice, diagnoses, and explanations provided by doctors.

Key Features:

• Doctor and Patient Roles: Each conversation is annotated with the role of the speaker (doctor or patient), making it easy to analyze communication patterns.
• Medical Context: The dataset includes diverse scenarios, from routine check-ups to more complex medical discussions, offering a broad spectrum of healthcare dialogues.
• Natural Language: The conversations are presented in natural language, allowing for the development and testing of NLP models focused on healthcare communication.
• Applications: This dataset can be used for various applications, such as building dialogue systems, analyzing communication efficacy, developing medical NLP models, and enhancing patient care through better understanding of doctor-patient interactions.

Potential Use Cases:

• NLP Model Training: Train models to understand and generate medical dialogues.
• Healthcare Communication Studies: Analyze communication strategies between doctors and patients to improve healthcare delivery.
• Medical Chatbots: Develop intelligent medical chatbots that can simulate doctor-patient conversations.
• Patient Experience Enhancement: Identify common patient concerns and doctor responses to enhance patient care strategies.

This dataset is a valuable resource for researchers, data scientists, and healthcare professionals interested in the intersection of technology and medicine, aiming to improve healthcare communication through data-driven approaches.

• Numerous language-centric research on healthcare is conducted day by day. To address shortcomings of Arabic natural language generation models, we introduce a large Arabic Healthcare Dataset (AHD) of textual data. For this motivation, we named our dataset ‘AHD’.

• The largest Arabic Healthcare Dataset (AHD) as we know was collected from altibbi website.

• The AHD consists of more than 808k Question and Answer into 90 variety categories. The AHD contains one file, and the file description will be discussed here. One file is the actual data which is in Arabic language.

  • AHD.xlsx file contains dataset in excel format, which includes the question, answer, and category in Arabic.

  • AHD_english.xlsx file contains dataset in excel format, which includes the question, answer, and category translated to English.

• Distribution of Question and Answer per category.xlsex shows the distribution of the data set by category.

Problem Statement

👉 Download the case studies here

Healthcare providers often rely on generalized treatment protocols that may not address the unique needs of individual patients. This approach led to variability in treatment outcomes, reduced efficacy, and limited patient satisfaction. A leading hospital sought a solution to develop personalized treatment plans tailored to each patient’s medical history, genetic profile, and current health status.

Challenge

Implementing a personalized healthcare treatment system involved overcoming the following challenges:

Integrating diverse patient data, including medical history, lab results, genetic information, and lifestyle factors.

Developing predictive models capable of identifying optimal treatment plans for individual patients.

Ensuring compliance with privacy regulations and maintaining data security throughout the process.

Solution Provided

An advanced healthcare treatment recommendation system was developed using machine learning models and predictive analytics. The solution was designed to:

Analyze patient data to identify patterns and predict treatment outcomes.

Recommend individualized treatment plans optimized for efficacy and patient preferences.

Continuously learn and adapt to improve recommendations based on new medical insights and patient feedback.

Development Steps

Data Collection

Aggregated data from electronic health records (EHR), genetic testing reports, and patient-provided health information.

Preprocessing

Standardized and anonymized data to ensure accuracy, consistency, and compliance with healthcare privacy regulations.

Model Development

Trained machine learning models to identify correlations between patient characteristics and treatment outcomes. Developed predictive algorithms to recommend personalized treatment plans for conditions like chronic diseases, cancer, and rare disorders.

Validation

Tested the system on historical patient data to evaluate its accuracy in predicting successful treatment outcomes.

Deployment

Integrated the solution into the hospital’s clinical decision support systems, enabling healthcare providers to access personalized treatment recommendations during consultations.

Continuous Monitoring & Improvement

Established a feedback mechanism to refine models using real-world treatment outcomes and patient satisfaction data.

Results

Improved Patient Outcomes

The system delivered personalized treatment recommendations that significantly improved recovery rates and health outcomes.

Increased Treatment Efficacy

Optimized treatment plans reduced trial-and-error approaches, leading to more effective interventions and fewer side effects.

Personalized Healthcare Experiences

Patients reported higher satisfaction levels due to treatment plans tailored to their individual needs and preferences.

Enhanced Decision-Making

Healthcare providers benefited from data-driven insights, enabling more informed and confident decisions.

Scalable and Future-Ready Solution

The system scaled seamlessly to support diverse medical specialties and adapted to incorporate emerging medical research.

Finding a good data source is the first step toward creating a database. Cardiovascular illnesses (CVDs) are the major cause of death worldwide. CVDs include coronary heart disease, cerebrovascular disease, rheumatic heart disease, and other heart and blood vessel problems. According to the World Health Organization, 17.9 million people die each year. Heart attacks and strokes account for more than four out of every five CVD deaths, with one-third of these deaths occurring before the age of 70. A comprehensive database for factors that contribute to a heart attack has been constructed. The main purpose here is to collect characteristics of Heart Attack or factors that contribute to it. The size of the dataset is 1319 samples, which have nine fields, where eight fields are for input fields and one field for an output field. Age, gender, heart rate (impulse), systolic BP (pressurehight), diastolic BP (pressurelow), blood sugar(glucose), CK-MB (kcm), and Test-Troponin (troponin) are representing the input fields, while the output field pertains to the presence of heart attack (class), which is divided into two categories (negative and positive); negative refers to the absence of a heart attack, while positive refers to the presence of a heart attack.

diagnose

This is a structured, multi-table dataset designed to simulate a hospital management system. It is ideal for practicing data analysis, SQL, machine learning, and healthcare analytics.

Dataset Overview

This dataset includes five CSV files:

1. patients.csv – Patient demographics, contact details, registration info, and insurance data

2. doctors.csv – Doctor profiles with specializations, experience, and contact information

3. appointments.csv – Appointment dates, times, visit reasons, and statuses

4. treatments.csv – Treatment types, descriptions, dates, and associated costs

5. billing.csv – Billing amounts, payment methods, and status linked to treatments

📁 Files & Column Descriptions

** patients.csv**

Contains patient demographic and registration details.

Column Description

patient_id -> Unique ID for each patient first_name -> Patient's first name last_name -> Patient's last name gender -> Gender (M/F) date_of_birth -> Date of birth contact_number -> Phone number address -> Address of the patient registration_date -> Date of first registration at the hospital insurance_provider -> Insurance company name insurance_number -> Policy number email -> Email address

** doctors.csv**

Details about the doctors working in the hospital.

Column Description

doctor_id -> Unique ID for each doctor first_name -> Doctor's first name last_name -> Doctor's last name specialization -> Medical field of expertise phone_number -> Contact number years_experience -> Total years of experience hospital_branch -> Branch of hospital where doctor is based email -> Official email address

appointments.csv

Records of scheduled and completed patient appointments.

Column Description

appointment_id -> Unique appointment ID patient_id -> ID of the patient doctor_id -> ID of the attending doctor appointment_date -> Date of the appointment appointment_time -> Time of the appointment reason_for_visit -> Purpose of visit (e.g., checkup) status -> Status (Scheduled, Completed, Cancelled)

treatments.csv

Information about the treatments given during appointments.

Column Description

treatment_id -> Unique ID for each treatment appointment_id -> Associated appointment ID treatment_type -> Type of treatment (e.g., MRI, X-ray) description -> Notes or procedure details cost -> Cost of treatment treatment_date -> Date when treatment was given

** billing.csv**

Billing and payment details for treatments.

Column Description

bill_id -> Unique billing ID patient_id -> ID of the billed patient treatment_id -> ID of the related treatment bill_date -> Date of billing amount -> Total amount billed payment_method -> Mode of payment (Cash, Card, Insurance) payment_status -> Status of payment (Paid, Pending, Failed)

Possible Use Cases

SQL queries and relational database design

Exploratory data analysis (EDA) and dashboarding

Machine learning projects (e.g., cost prediction, no-show analysis)

Feature engineering and data cleaning practice

End-to-end healthcare analytics workflows

Recommended Tools & Resources

SQL (joins, filters, window functions)

Pandas and Matplotlib/Seaborn for EDA

Scikit-learn for ML models

Pandas Profiling for automated EDA

Plotly for interactive visualizations

Please Note that :

All data is synthetically generated for educational and project use. No real patient information is included.

If you find this dataset helpful, consider upvoting or sharing your insights by creating a Kaggle notebook.

This dataset contains demographic and personal health information for individuals, along with the corresponding medical insurance charges billed to them. It is commonly used to build predictive models for insurance costs and to explore relationships between factors such as age, BMI, smoking status, and region on medical expenses.

Features: - age: Age of the primary beneficiary (integer) - sex: Gender of the individual (male, female) - bmi: Body mass index, providing a measure of body fat based on height and weight (float) - children: Number of children/dependents covered by the insurance (integer) - smoker: Smoking status of the individual (yes, no) - region: Residential area in the US (northeast, northwest, southeast, southwest) - charges: Individual medical costs billed by health insurance (float, in USD)

Applications: This dataset is frequently used in regression modeling, cost prediction, and data visualization tasks. It is ideal for learning how lifestyle and demographic factors impact healthcare expenses and serves as a foundational dataset for applied machine learning in health economics.

This dataset is made available under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). See LICENSE.pdf for details.

Dataset description

Parquet file, with:

• 35694 rows
• 154 columns

The file is indexed on [participant]_[month], such that 34_12 means month 12 from participant 34. All participant IDs have been replaced with randomly generated integers and the conversion table deleted.

Column names and explanations are included as a separate tab-delimited file. Detailed descriptions of feature engineering are available from the linked publications.

File contains aggregated, derived feature matrix describing person-generated health data (PGHD) captured as part of the DiSCover Project (https://clinicaltrials.gov/ct2/show/NCT03421223). This matrix focuses on individual changes in depression status over time, as measured by PHQ-9.

The DiSCover Project is a 1-year long longitudinal study consisting of 10,036 individuals in the United States, who wore consumer-grade wearable devices throughout the study and completed monthly surveys about their mental health and/or lifestyle changes, between January 2018 and January 2020.

The data subset used in this work comprises the following:

• Wearable PGHD: step and sleep data from the participants’ consumer-grade wearable devices (Fitbit) worn throughout the study
• Screener survey: prior to the study, participants self-reported socio-demographic information, as well as comorbidities
• Lifestyle and medication changes (LMC) survey: every month, participants were requested to complete a brief survey reporting changes in their lifestyle and medication over the past month
• Patient Health Questionnaire (PHQ-9) score: every 3 months, participants were requested to complete the PHQ-9, a 9-item questionnaire that has proven to be reliable and valid to measure depression severity

From these input sources we define a range of input features, both static (defined once, remain constant for all samples from a given participant throughout the study, e.g. demographic features) and dynamic (varying with time for a given participant, e.g. behavioral features derived from consumer-grade wearables).

The dataset contains a total of 35,694 rows for each month of data collection from the participants. We can generate 3-month long, non-overlapping, independent samples to capture changes in depression status over time with PGHD. We use the notation ‘SM0’ (sample month 0), ‘SM1’, ‘SM2’ and ‘SM3’ to refer to relative time points within each sample. Each 3-month sample consists of: PHQ-9 survey responses at SM0 and SM3, one set of screener survey responses, LMC survey responses at SM3 (as well as SM1, SM2, if available), and wearable PGHD for SM3 (and SM1, SM2, if available). The wearable PGHD includes data collected from 8 to 14 days prior to the PHQ-9 label generation date at SM3. Doing this generates a total of 10,866 samples from 4,036 unique participants.