ROLE OF BIG DATA IN MEDICAL DIAGNOSTICS

ELQ 301 PRESENTATIONNISHANT AGARWAL 2014EE10464

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

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

BIG DATA IN HEALTHCARE"Medical diagnostics, at heart, is a data problem"

Potential to improve quality of healthcare meanwhile reducing costs

Source- MANA

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

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

BIG DATA IN MEDICAL DIAGNOSTICS OF

CARDIOVASCULAR DISEASES

STATUS QUORural 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

INDICATORS OF HEALTH

MOST USEFUL PARAMETERS

Source: Ohio State University

HEART RATE VARIABILITY

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

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 Paramete

rs

DATA

ANALYSI

S

OUTPUT

Diagnosi

s

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.

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.

OUTPUTo 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

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

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

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