The market for healthcare analytics is likely to reach $75.1 billion by 2026.
The demand for more qualified data scientists has been fueled by the growing popularity of big data and its potential impact on the healthcare sector. Because of this, many big data and data science experts are willing to switch to the healthcare sector.
But wait. How do you hone your skills to become an ideal candidate for the healthcare data scientist role? By working on unique real-world healthcare projects. This blog is here to help you explore the five topmost machine learning projects in healthcare.
Below are five popular machine learning projects in healthcare that you must work on if you are planning to switch to any healthcare data science role.
Healthcare organizations can provide personalized patient care by using deep-learning solutions to analyze test results, symptoms, and medical histories. Furthermore, based on a patient's symptoms and genetic information from his medical history, doctors can predict the risks and threats to his health. The most relevant medical treatments are determined using natural language processing (NLP), which analyzes free-text medical sources. Other techniques that can be used to create patient self-management tools include Support Vector Machine (SVM), Random Forest, and k-nearest neighbor. You can predict the results of the individual treatment with the help of logistic regression and multilayer perceptrons.
Recognizing chronic diseases like diabetes can help clinicians make better decisions about patient care, which can ultimately improve patient outcomes. The goal of this project is to assess whether a patient hospitalized in an intensive care unit (ICU) has Diabetes Mellitus by analyzing data from the first 24 hours of admission. Both demographic information about the patient and clinical parameters from the first 24 hours post their ICU admission will be used for this project. You can use classifiers like LightGBM, XGBoost, CatBoost, and Deep Neural Networks in an ensemble learning approach.
Automatic image segmentation is a crucial stage in the process of gathering valuable data from medical images that can help in diagnosis. For this project idea, you will use a convolutional neural network (CNN) to segment images in order to identify blood vessels in retinal scans. Use the DRIVE (Digital Retinal Images for Vessel Extraction) data set for this machine learning project. You can compare a model that has already been trained using ImageNet VGG encoder + data augmentation to other iterations of the model.
The major objective of this project idea is to eliminate any existing loopholes in hospital patient monitoring. Deep convolutional neural network (CNN) technology and mmWave radar can be useful in this cutting-edge patient behavior detection system to facilitate the simultaneous real-time recognition of different patient behaviors. You will track many patients in this project and determine their individual scattering point clouds using a mmWave radar. Build a three-layer CNN model to categorize each patient's behavior. To gather the Doppler pattern and execute the CNN model, implement the tracking and point clouds detection technique using a mmWave radar hardware platform with an embedded graphics processing unit (GPU) board.
The growing prevalence of ASD cases and the economic impact of autism on society worldwide highlight the urgent need for the adoption of simple screening approaches. Use the UCI repository's public Autistic Spectrum Disorder Screening Data for Adult dataset for this project. You will implement various supervised learning models in this project, such as Random Forest, Support Vector Machines (SVM), K-Nearest Neighbors (KNeighbors), Gaussian Naive Bayes (GaussianNB), Logistic Regression, etc. Additionally, you will build a model employing the MultiLayer Perceptron sequential model architecture (MLP).
You can also check out platforms like GitHub and Kaggle for more project ideas around machine learning in healthcare.
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