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Information
| Unit | INSTITUTE OF MEDICAL SCIENCES |
| BIOSTATISTICS (MEDICINE) (MASTER) (WITH THESIS) | |
| Code | BİS506 |
| Name | Applications of Machine Learning in Health Sciences |
| Term | 2026-2027 Academic Year |
| Term | Fall and Spring |
| Duration (T+A) | 1-3 (T-A) (17 Week) |
| ECTS | 4 ECTS |
| National Credit | 2.5 National Credit |
| Teaching Language | Türkçe |
| Level | Belirsiz |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Dr. Öğr. Üyesi Sevinç Püren YÜCEL KARAKAYA |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
The objective of this course is to teach the basic principles of machine learning methods in health data and to enable students to gain the skills to analyze data using appropriate algorithms, develop models, evaluate results, and apply these methods to health-related problems.
Course Content
This course covers the structure of health data, data preprocessing methods, supervised and unsupervised learning techniques, classification and regression methods, model performance evaluation metrics, feature selection, cross-validation, prediction models in health data, and machine learning applications.
Course Precondition
No
Resources
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: with applications in R (Vol. 103). New York: 3 springer.
Notes
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: with applications in R (Vol. 103). New York: 3 springer.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explain the basic concepts of machine learning. |
| LO02 | Define the characteristics of health data and analysis processes. |
| LO03 | Apply data preprocessing and feature selection methods. |
| LO04 | Build classification and regression models. |
| LO05 | Evaluate model performance using appropriate metrics. |
| LO06 | Apply and interpret machine learning methods in health data. |
| LO07 | Sonuçları bilimsel biçimde raporlar. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Students comprehends the fundamentals of statistical theory related to the field of health ( probability and bayesian biostatistics). | |
| PLO02 | Bilgi - Kuramsal, Olgusal | Students explain demographic terminologies and statistical methods in the field of health sciences. | |
| PLO03 | Beceriler - Bilişsel, Uygulamalı | Students understand and use medical terminology. | |
| PLO04 | Beceriler - Bilişsel, Uygulamalı | Students collect data from research studies, analyze, and make inferences | 1 |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Students knows the system of international classification of diseases, obtain and analyze hospital statistics. | |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Students design scientific research studies in order to give response to the problem arising from health and clinical sciences | 3 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Students select the appropriate statistical procedure for analysis , apply and make inferences. | 2 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Students use the necessary statistical packages for analysis, if necessary write and develop software. | 2 |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Students follow the latest development in medical informatics and employ frequently used tools and methods. | 5 |
| PLO10 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Students design health survey, determine the sampling method and conduct the survey | |
| PLO11 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Students select and use proper statistical procedure for diagnosis and in making inferences for the data in health and clinical medicine and provide consultance to clinicians in the field. | 4 |
| PLO12 | Yetkinlikler - Öğrenme Yetkinliği | Students develop the ability of critical thinking, make a conclusion with a critical approach to the evidence | |
| PLO13 | Yetkinlikler - Öğrenme Yetkinliği | Students apply analytical procedure to frequently used survival data, multivariate procedure and regression techniques. | 2 |
| PLO14 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Students provide consulting services by using effective communication skills; take part in research teamworks; defend the ethical rules. | |
| PLO15 | Yetkinlikler - Alana Özgü Yetkinlik | Students explain the fundamental terminologies in epidemiology, guide researchers conducting field survey and clinical studies, develop methodologies in determining disease risk factor and disease burden and advise for choosing proper diagnostic test. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to Machine Learning and Basic Concepts | Reading | Öğretim Yöntemleri: Anlatım |
| 2 | Structure of Health Data and Data Types | Reading | Öğretim Yöntemleri: Anlatım |
| 3 | Data Preprocessing Methods | Reading | Öğretim Yöntemleri: Anlatım |
| 4 | Feature Selection and Dimensionality Reduction | Reading | Öğretim Yöntemleri: Anlatım |
| 5 | Classification Methods I | Reading | Öğretim Yöntemleri: Anlatım |
| 6 | Classification Methods II | Reading | Öğretim Yöntemleri: Anlatım |
| 7 | Model Performance Evaluation Metrics | Reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 8 | Mid-Term Exam | Ölçme Yöntemleri: Yazılı Sınav |
|
| 9 | Regression Methods I | Reading | Öğretim Yöntemleri: Anlatım |
| 10 | Regression Methods II | Reading | Öğretim Yöntemleri: Anlatım |
| 11 | Clustering Methods | Reading | Öğretim Yöntemleri: Anlatım |
| 12 | Cross-Validation and Model Selection | Reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 13 | Machine Learning Applications in Health Data I | Reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 14 | Machine Learning Applications in Health Data II | Reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 15 | Interpretation and Reporting of Results | Reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 16 | Term Exams | Ölçme Yöntemleri: Yazılı Sınav |
|
| 17 | Term Exams | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 4 | 56 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 3 | 42 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 4 | 4 |
| Final Exam | 1 | 4 | 4 |
| Total Workload (Hour) | 106 | ||
| Total Workload / 25 (h) | 4,24 | ||
| ECTS | 4 ECTS | ||