Healthcare Big Data Analysis with Artificial Neural Network for Cardiac Disease Prediction
Abstract
:1. Introduction
2. Related Work
2.1. Limitations of Existing Works
2.2. Contributions
- An advanced functional cloud-based big data analytic architecture is proposed to process the massive volume of manifold medical data.
- A multi-modal analysis is performed considering patient’s demographic, physiological and ECG data as feature attributes.
- ECG signal analysis is performed considering important feature points to identify the normal and abnormal heart condition.
- An intelligent prognosis model is developed by integrating the concept of ANN and PSO to classify the heart disease considering multi-parametric data collected from symptoms, reports and ECG records.
- The proposed intelligent system has achieved a maximum 99% accuracy in determining the cardiac abnormality in critical cases.
- Extensive simulation is performed to validate the proposed cloud-based big data analytic framework and heart disease prediction algorithm.
3. Framework for Cardiological Data Processing and Analysis
3.1. Structural Architecture
3.2. Logical Architecture
3.2.1. User Layer
Data Sources (DS)
Data Types (DTs)
3.2.2. Cloud Layer
Request Handler (RH)
3.2.3. Big Data Layer
Data Management (DM)
Data Integration (DI)
Data Analyzer (DA)
Data Visualization (DV)
3.2.4. Physical Storage Layer
4. Intelligent Model for Cardiac Disease Prediction
4.1. Environmental Scenario of Cardiac Healthcare Big Data
4.2. Delineation of ECG Feature Points
Algorithm 1 ECG feature point analysis in Hadoop cluster |
|
4.3. Cardiac Disease Prediction Using ANN and PSO
4.3.1. Artificial Neural Network
4.3.2. Particle Swarm Optimization
Algorithm 2 APSO algorithm |
|
5. Results and Discussions
5.1. Simulation Setup
5.2. Simulation Result
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Related Works | Big Data | Map Reduce | Cloud | Cardiac Healthcare Data | ECG Data |
---|---|---|---|---|---|
[16] | ✓ | × | ✓ | ✓ | × |
[19] | ✓ | ✓ | ✓ | ✓ | × |
[2,22] | × | × | × | × | ✓ |
[31] | ✓ | × | ✓ | ✓ | × |
[32,33] | ✓ | ✓ | × | × | × |
[34] | × | ✓ | ✓ | × | × |
[35,36] | ✓ | × | × | ✓ | × |
[37,38] | × | × | ✓ | ✓ | × |
[39,40,41] | × | × | × | ✓ | × |
Ours | ✓ | ✓ | ✓ | ✓ | ✓ |
Related Works | Dataset | Algorithm Type | Analysis | # of Features | Accuracy |
---|---|---|---|---|---|
[40] | UCI repository | Learning vector quantization (LVQ) | Classification | 10 | 0.98 |
[41] | Cleveland heart disease | MMC, Random, Adaptive, QUIRE, and AUDI | Classification | 14 | 0.57 |
[42] | Cleveland heart disease | Multilayer perceptron (MLP) + PSO | Classification | 13 | 0.84 |
[43] | Cleveland heart disease | Recurrent neural network (RNN) + Long short term memory (LSTM) | Classification | 14 | 0.95 |
[44] | Cardiovascular disease (CVD) and Framingham | MLP, Support vector classifier (SVC) | Classification | 12 (CVD), 11 (Fram) | 0.74 (CVD), 0.71 (Fram) |
[45] | Cleveland heart disease | SVM + AdaBoost | Classification | 14 | 0.88 |
[46] | Cleveland, Hungarian, Switzerland, Long Beach VA, Statlog Data Set | Classification and regression tree (CART) | Classification and Regression | 11 | 0.87 |
[47] | Department of Cardiology, IGMC | Multinomial logistic regression (MLR) | Classification | 26 | 0.98 |
Attributes | Descriptions | Original Values | Normalized Values |
---|---|---|---|
Age | Age in year | Continuous | 0: age ≤ 30 1: 30 ≥ age < 50 2: 50 ≥ age < 70 3: age ≥ 70 |
Sex | Male or Female | (0, 1) | 0: Female 1: Male |
cp | Chest pain | 1: Typical angina 2: Atypical pain 3: Non-anginal pain 4: Asymptomatic | 1: Typical angina 2: Atypical pain 3: Non-anginal pain 4: Asymptomatic |
trestbps | Resting blood pressure | Continuous | Normalization using min–max method |
chol | Serum cholesterol in mg/dL | Continuous | 0: chol ≤ 200 1: 200 > chol ≤ 239 2: chol ≥ 239 |
fbs | Fasting blood sugar | Continuous | 0: fbs ≤ 120 1: fbs > 120 |
restecg | Resting electro-cardiographic | (0, 1, 2) | (0, 1, 2) |
thalach | Maximum heart rate | Continuous | Normalization using min–max method |
exang | Exercise-induced angina | Yes or No | 0:No 1:Yes |
oldpeak | ST depression | (0–4) | (0–4) |
slope | Slope of peak exercise ST segment | 1: Upsloping 2: Flat 3: Downsloping | 1: Upsloping 2: Flat 3: Downsloping |
ECG abnormality | Abnormality in any feature points | Yes or No | 0:No 1:Yes |
ca | Number of major vessels (0–3) colored by fluoroscopy | (0–3) | (0–3) |
thal | Normal, Fixed defect, Reversible defect | 3: Normal 6: Fixed defect 7: Reversible 7: defect | 3: Normal 6: Fixed defect 7: Reversible 7: defect |
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Mohapatra, S.; Sahoo, P.K.; Mohapatra, S.K. Healthcare Big Data Analysis with Artificial Neural Network for Cardiac Disease Prediction. Electronics 2024, 13, 163. https://doi.org/10.3390/electronics13010163
Mohapatra S, Sahoo PK, Mohapatra SK. Healthcare Big Data Analysis with Artificial Neural Network for Cardiac Disease Prediction. Electronics. 2024; 13(1):163. https://doi.org/10.3390/electronics13010163
Chicago/Turabian StyleMohapatra, Sulagna, Prasan Kumar Sahoo, and Suvendu Kumar Mohapatra. 2024. "Healthcare Big Data Analysis with Artificial Neural Network for Cardiac Disease Prediction" Electronics 13, no. 1: 163. https://doi.org/10.3390/electronics13010163
APA StyleMohapatra, S., Sahoo, P. K., & Mohapatra, S. K. (2024). Healthcare Big Data Analysis with Artificial Neural Network for Cardiac Disease Prediction. Electronics, 13(1), 163. https://doi.org/10.3390/electronics13010163