Big data in healthcare refers to large and complex electronic health data sets from various sources. Big data analytics has the potential to improve medical diagnostics and reduce healthcare costs. It can help address doctor shortages by assisting physicians in decision making. For cardiovascular diseases, useful parameters like heart rate variability can be analyzed from patient data along with other indicators. Machine learning algorithms are applied to the large datasets to find patterns, classify diseases, and predict diagnoses. This can help develop decision support systems and cross-platform diagnostic tools to benefit patients. Challenges include achieving high accuracy, handling data issues, and minimizing the diagnostic process.

1. ROLE OF BIG DATA IN MEDICAL DIAGNOSTICS
ELQ 301 PRESENTATION
NISHANT AGARWAL
2014EE10464

2. INDEX
 What is Big data in healthcare?
 Need for Big Data Analytics
 Big Data in Medical Diagnostics of Heart Diseases
 Process of Medical Diagnostics
 Applications
 Challenges

3. WHAT IS BIG DATA?
 Big data in healthcare refers to large and complex electronic health data sets
Huge volume and diversity of data types
 Includes data from clinical decision support systems (medical imaging, EPRs etc.)
The totality of data related to patient healthcare make up “Big Data” in healthcare

4. BIG DATA IN HEALTHCARE
"Medical diagnostics, at heart, is a data problem"
Potential to improve quality of healthcare
meanwhile reducing costs
Source- MANA

5. Need for Big Data Analytics in HealthCare
 Data mining at times has proven to predict the diseases better than the physicians
 Huge volume and variety of data which can’t be handled by traditional methods
 Reduce the clinical and economic burden of healthcare

6. Need for Big Data Analytics in HealthCare
 Address shortage of doctors and assist doctors in decision making
 Can be used for self-diagnosis or pre-diagnosis in hospitals
 Self-diagnosis:
 Make clinical decision support system accessible to all even in remote area
 To make ill-informed patients more informed about their health status

7. BIG DATA IN MEDICAL DIAGNOSTICS OF
CARDIOVASCULAR DISEASES

8. STATUS QUO
Rural India faces a shortage of more than 60% doctors
30 million heart patients in India according to WHO
500 petabytes of available Healthcare Data
Big-data is the way forward
Source- indiatimes/ TOI

9. INDICATORS OF HEALTH

10. MOST USEFUL PARAMETERS
Source: Ohio State University
HEART RATE VARIABILITY

11. HEART RATE VARIABILITY
 HRV is the physiological phenomenon of variation in time interval between heartbeats
 One of the most promising quantitative markers of autonomic activity
 Widely applied in basic and clinical research studies
Ref- http://www.myithlete.com/what-is-hrv, https://en.wikipedia.org/wiki/Heart_rate_variability

12. PROCESS OF MEDICAL DIAGNOSTICS
• Input patient’s data related to relevant parameters such as HRV, BMI etc.
• Analyse and compare the data using ML algorithms on Database of Parameters
• Prediction/ Diagnosis of cardiovascular diseases
INPUT
Data of
Parameters
DATA ANALYSIS
OUTPUT
Diagnosis

13. INPUT PARAMETERS
 Basic info about patient such as Age, BMI, Smoking Status
 Get HRV data of patient using ECG or some wearable devices
 Input blood cholesterol, glucose level, MRI data
Ref: Analysis of Supervised Machine Learning Algorithms for Heart Disease Prediction by Ayon Dey et al.

14. ANALYTICS OF DATA
 Time Domain/ Frequency Domain Analysis of HRV Data like SDNN
 Apply ML Algorithms like SVM, Naive Bayes, Decision Tree, Principal Component
Analysis to the Big Data Sets to find patterns and classify and predict the diseases
 PCA can be used to reduce the number of attributes, SVM can further be used to
predict heart disease
Ref: Analysis of Supervised Machine Learning Algorithms for Heart Disease Prediction by Ayon Dey et al.

15. OUTPUT
o Diagnose heart as healthy or predict possible diseases
o Classification of disease as chronic, coronary heart disease, inflammatory heart disease
o Recommend further action or tests to confirm the disease

16. APPLICATIONS
 Decision Support System to assist doctors in decision making
 Cross platform systems can be developed to be adopted to smartphones, kiosks etc.
Image Courtesy appleinsider.com

17. CHALLENGES
 Getting high Diagnostic accuracy on new cases from available data
 Dealing with missing and noisy data
 Reducing the number of tests required for diagnosis
 Minimising Time complexity of the whole process from acquisition to decision making

18. THANK YOU