The demand for Machine Learning Engineers/AI specialists in the field of Medicine, Healthcare, and Public Health experiencing rapid growth.
Start a career in Medical/Healthcare with the Techtern Applied ML in Medical Diagnosis and build a robust foundation for a great career
Problem Statement :
Can we accelerate the capacity and speed of medical professionals to diagnose diseases from X-rays and MRI brain scans? Achieving this capability can potentially minimize the cases of misdiagnosis, long wait times for patients, and clinic throughput
Aim :
This project portfolio consists of two projects designed to improve your mastery of ML-based mdeical diagnosis
In the first project, you will develop an ML model that detects disease conditions from chest X-rays.
A second project will improve your skill in developing models to identify, locate, and size tumors from MRI scans
Project Contents :
Dataset Information
Image pre-processing and Exploratory Data Analysis (EDA)
Modeling
Conclusion
What you will learn :
Application of Deep learning techniques for chest X-Ray Medical Diagnosis
Evaluation Techniques of Diagnostic Models
Brain Tumor Auto-Segmentation for Magnetic Resonance Imaging (MRI)
Heart failure prediction project focused on developing a full-stack deployable machine learning model to predict the onset of heart failure in patients.
This project includes the development of a core model and the implementation of a chatbot complete with a front and back end and API endpoints for serving the model to patients and the backend for sending notifications to doctors
According to the World Health Organization, 12 million deaths occur yearly due to heart disease. Load of cardiovascular disease is rapidly increasing all over the world in the past few years. Early detection of cardiac diseases can decrease the mortality rate and overall complications. However, it is not possible to monitor patients every day in all cases accurately and consultation with a patient for 24 hours by a doctor is not available since it requires more patience, time and expertise.
Our Heart Failure Prediction System is intended to assist patients in recognizing their heart state early and receiving treatment at an earlier stage, allowing them to avoid any serious conditions. We have designed this system using the Machine Learning model to predict the future possibility of heart disease by implementing the Logistic Regression algorithm.
The framework used in this project is Django. The Front End involves Html, CSS and JavaScript. The Back End involves MySQL Database. The Back End Language is Python
The user would need to register first to log into the system. For the system to predict if there is heart failure or not, the user would require to give inputs. The parameters include Age, Sex, Chest Pain Type, Resting BP, Cholesterol, Fasting BS, Resting ECG, Maximum Heart Rate, Exercise-induced Agina, Oldpeak, and the slope of the peak exercise ST segment. After the user provides all these inputs, the system will detect if there is any heart disease. The chatbot in the system will inform the user about the causes of heart failure, and the diagnosis test required. It will also provide links to nearby hospitals/clinics that specializes in heart disease. The user can also check out some free checkup camps.
The admin can log in using their credentials. They can view the users using the system. They can also add free checkup camp details. We have used Logistic Regression to develop this system. It is a significant machine learning algorithm because it can provide probabilities and classify new data using continuous and discrete datasets.
Advantages
- The system is easy to maintain.
- It is user-friendly.
- Users can search for a doctor’s help at any point in time.
- Very useful in case of emergency.
- They can also look for free check-up camps.
Requirements:
Basic knowledge of python required
Learn how to organize and implement a classification model to predict if a patient is prone to heat failure.
You will work with dirty data and progressively transform it into clean data, learn to conduct simple feature engineering, and master 5 algorithms
Problem Statement :
With a plethora of medical data available and the rise of Data Science, a host of startups are taking up the challenge of attempting to create indicators for the foreseen diseases that might be contracted! Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worldwide. Heart failure is a common event caused by CVDs. People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidemia or already established disease) need early detection and management wherein a machine learning model can be of great help. In this way, we try to solve and automate another problem that occurs in nature with a view to counter it and focus on the next problem with the help of AI techniques!
Aim :
- Classify/predict whether a patient is prone to heart failure depends on multiple attributes.
- It is a binary classification with multiple numerical and categorical features.
- Age: age of the patient [years]
- Sex: sex of the patient [M: Male, F: Female]
- ChestPainType: chest pain type [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic]
- RestingBP: resting blood pressure [mm Hg]
- Cholesterol : serum cholesterol [mm/dl]
- FastingBS: fasting blood sugar [1: if FastingBS > 120 mg/dl, 0: otherwise]
- RestingECG: resting electrocardiogram results [Normal: Normal, ST: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV), LVH: showing probable or definite left ventricular hypertrophy by Estes' criteria]
- MaxHR: maximum heart rate achieved [Numeric value between 60 and 202]
- ExerciseAngina: exercise-induced angina [Y: Yes, N: No]
- Oldpeak : old peak = ST [Numeric value measured in depression]
- ST_Slope: the slope of the peak exercise ST segment [Up: upsloping, Flat: flat, Down: downsloping]
- HeartDisease: output class [1: heart disease, 0: Normal]
Project Contents :
- Dataset Information
- Exploratory Data Analysis (EDA)
- Summary of EDA
- Modeling
- Conclusion
What you will learn :
- Data Visualization
- Data Scaling
- Statistical Tests for Feature Engineering
- Modeling and visualization of results for algorithms
Requirement :
Basic knowledge of Python required
Build your skills in the application of machine learning in healthcare and medical diagnostics. This project will build your mastery of the key stages of analytics and predictive modeling in healthcare as you work with our project team to design, develop and implement a breast cancer diagnostic system
Problem Statement :
The application of machine learning approaches to medical diagnostics has grown explosively in recent years. The breast cancer diagnostics project enables participants to build intermediate-level skills in the application of data science and machine learning-based approaches to improving the quality and speed of medical diagnostics in patient care across the healthcare delivery spectrum
Aim :
This project aims to create mastery in candidates to build a model able to predict the diagnosis of breast cancer tissues as malignant or benign. Participants will learn how to combine biology, chemistry, physics, and data science to uncover insights or find previously hidden patterns which in this case will be to predict whether a cancer cell is malignant or benign
Data for this project is drawn from digitized images of a fine needle aspirate (FNA) of a breast mass that describe characteristics of the cell nuclei present in the image
What you will learn :
- Data Analytics toolset for health/medical diagnostics
- Data Cleaning and Feature Engineering
- Multiple Modeling Approaches for Medical data features
- Model Performance Tunning
- API-based model serving
This project features JIT-based training in Python and as such, candidates are not required to possess any skills in Python