BM25C06 Healthcare Data Analytics – Semester III – BME / MedElec – R-2025

Subject Code & Name: BM25C06 – Healthcare Data Analytics

Regulation: R-2025

Semester: III (Third Semester)

Branch: B.E. Biomedical Engineering (BME) / B.E. Medical Electronics (MedElec)

Credits / L-T-P: 3 Credits | L-T-P: 2-0-2

Course Objectives

• To comprehend the fundamental of mathematical and statistical theory in the application of Healthcare, Apply the regression and correlation analysis on the healthcare data an Understand the Meta analysis and variance analysis, Interpret the results of the investigational methods.

Full Unit-wise Syllabus

Unit I Introduction

Electronic Health record- Components, Coding systems- International classification of diseases (ICD), LOINC, DICOM, Introduction to probability, likelihood & odds, distribution variability.

Unit II – Statistical Parameters

p-values, computation, level chi square test and distribution and hypothesis testing - single population proportion, difference between two population proportions, single population variance, tests of homogeneity. Testing of statistical parameters using appropriate software R / Python.

Practical: Finding the statistical distribution using appropriate software tool like R/ Python. Perform basic data exploration and visualization of a healthcare dataset

Unit III – Regression and Correlation Analysis

Regression model, evaluating the regression equation, correlation model, correlation coefficient.

Practical: Finding regression, correlation for the data using appropriate software like R / Python. Testing the variance using appropriate software tool like R / Python.

Unit IV – Analysis Of Variance

META analysis for research activities, purpose and reading of META analysis, kind of data used for META analysis, completely randomized design, randomized complete block design, repeated measures design, factorial experiment.

Practical: Analysis of data from wearables devices. Testing the variance using appropriate software tool like R / Python.

Unit V – Case Studies

Epidemical reading and interpreting of epidemical studies, application in community health, Case study on Medical Imaging like MRI, CT. Case study on respiratory data, Case study on ECG data. Predictive models to detect onset of disease

Activities: Project based learning- Implement data analytics on healthcare data using ‘R’ software.

Course Outcomes (COs)

• CO1: Explain fundamental concepts of probability, statistical parameters, and coding systems in healthcare data (ICD LOINC, DICOM).
• CO2: Analyze healthcare datasets using statistical tests, p values, chi square tests, and hypothesis testing with R/Python.
• CO3: Apply regression and correlation analysis to healthcare data for predictive modeling and interpretation.
• CO4: Conduct meta analysis, ANOVA, and interpret experimental and clinical study results for informed decision making.
• CO5: Implement data analytics projects on real world healthcare datasets using R/Python to generate actionable insights.

Assessment Pattern (Quick Note)

• Weightage: Continuous Assessment 50% | End Semester Examinations 50%
• Internal methodology: Project (20%), Assignment Programs (25%), Practical (25%), Internal Examinations (30%)

Source: Official Anna University – B.E. Biomedical Engineering R-2025 Curriculum
Last Updated: September 2026