AI for Healthcare
AI for Healthcare

AI for Healthcare

This post provides a link to my Github repository for my submissions for Udacity's AI for Healthcare Nanodegree Program. I learned to build, evaluate, and integrate predictive models that have the power to transform patient outcomes and uses AI for Healthcare.  I started by classifying and segmenting 2D and 3D medical images to augment diagnosis and then moved on to modelling patient outcomes with electronic health records to optimize clinical trial testing decisions. Finally, I build an algorithm that uses data collected from wearable devices to estimate the wearer's pulse rate in the presence of motion. Applying AI to 2D Medical Imaging Data…

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Classification of Alzheimer’s Disease Stages using Radiology Imaging and Longitudinal Clinical Data – References

  References for the project C. Aditya and M. S. Pande. Devising an interpretable calibrated scale to quantitatively assess the dementia stage of subjects with alzheimer’s disease: A machine learning…

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Classification of Alzheimer’s Disease Stages using Radiology Imaging and Longitudinal Clinical Data – Part 10

Discussion The project has fully answered research questions after implementing different machine learning techniques and evaluating the performance of the models. The developed model and user-friendly web application contribute to…

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Classification of Alzheimer’s Disease Stages using Radiology Imaging and Longitudinal Clinical Data – Part 9

Use Feature Selection Techniques and Build an Ensemble of Classification Models Feature selection is an automatic or manual process to select features which contribute to the prediction and remove irrelevant…

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Classification of Alzheimer’s Disease Stages using Radiology Imaging and Longitudinal Clinical Data – Part 8

Fine Tuning of an Ensemble of Classification Models Using Random Grid Search Parameters for a model are learned during the training while hyperparameters are set to control the implementation of…

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Classification of Alzheimer’s Disease Stages using Radiology Imaging and Longitudinal Clinical Data – Part 7

Create a Web-based Application using XGBoost The implementation uses six features which are important to classify the stages of the disease and XGBoost to develop the web-based application. The features…

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Classification of Alzheimer’s Disease Stages using Radiology Imaging and Longitudinal Clinical Data – Part 6

Build a Classification Model using Tree-Based Algorithms In this implementation, selected features to build classification model include subject’s education, gender, age and a small list of biomarkers as discussed in…

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Classification of Alzheimer’s Disease Stages using Radiology Imaging and Longitudinal Clinical Data – Part 5

Build a Classification Model using Monthly Changes in Radiology Imaging and Clinical Data Multiple studies such as (Lahmiri and Shmuel, 2018) and (Zhang and Sejdi´c, 2019) typically apply various machine…

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