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.
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Role of Big Data in Medical Diagnostics
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
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
Availability of massive quantities of data (known as ‘Big Data’)
Petabyte? 10^15
Make sense of huge data
Potential to address Devices employing big data can be used
Now I will tell how
Look into application of Big Data in medical diagnosis of heart diseases
Cardiovascular diseases account for around one fourth
of all deaths due to non- communicable diseases in India
Big Data can be used to address the shortage of doctors by use in medical diagnosis devices
To address these challenges Big data is the way forward
Some easy to get, some require special instruments, availability and tests
Most imp parameters to dignose heart disease, one of the most important and readily available is HRV
60 considered good avg of RR interval of pulses
"RR variability" (where R is a point corresponding to the peak of the QRS complex of the ECG wave; and RR is the interval between successive Rs), and "heart period variability
More than heart rate HRV gives a better indicator of health of the heart
As many commercial devices now provide an automated measurement of HRV, the cardiologist has been provided with a seemingly simple tool for both research and clinical studies.
Now that we have seen some useful parameters let us look at how we can diagnose diseases using these
(Database of Parameter
such as HRV,BMI etc.)
Black Box
(M.L. Algorithms)
Lets look in detail into each of the steps involved
1 and 2 nd in remote areas possible,
3rd in urban areas
Support vector machines
(Standard Deviation of NN interval)
In medical diag
Dealing with noisy data : Medical data typically suffer from uncertainty and errors. Therefore machine learning algorithms appropriate for medical applications have to have effective means for handling noisy data.
nosis very often the description of patients in patient 5 records lacks certain data.
It is desirable to have a classifier that is able to reliably diagnose with a small amount of data about the patients. In medical practice, the collection of patient data is often expensive, time consuming, and harmful for the patients.