DATA SCIENCE WORKSHOP: Heart Failure Analysis and Prediction Using Scikit-Learn, Keras, and TensorFlow with Python GUI

DATA SCIENCE WORKSHOP: Heart Failure Analysis and Prediction Using Scikit-Learn, Keras, and TensorFlow with Python GUI
Author :
Publisher : BALIGE PUBLISHING
Total Pages : 398
Release :
ISBN-10 :
ISBN-13 :
Rating : 4/5 ( Downloads)

Book Synopsis DATA SCIENCE WORKSHOP: Heart Failure Analysis and Prediction Using Scikit-Learn, Keras, and TensorFlow with Python GUI by : Vivian Siahaan

Download or read book DATA SCIENCE WORKSHOP: Heart Failure Analysis and Prediction Using Scikit-Learn, Keras, and TensorFlow with Python GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-08-18 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this "Heart Failure Analysis and Prediction" data science workshop, we embarked on a comprehensive journey through the intricacies of cardiovascular health assessment using machine learning and deep learning techniques. Our journey began with an in-depth exploration of the dataset, where we meticulously studied its characteristics, dimensions, and underlying patterns. This initial step laid the foundation for our subsequent analyses. We delved into a detailed examination of the distribution of categorized features, meticulously dissecting variables such as age, sex, serum sodium levels, diabetes status, high blood pressure, smoking habits, and anemia. This critical insight enabled us to comprehend how these features relate to each other and potentially impact the occurrence of heart failure, providing valuable insights for subsequent modeling. Subsequently, we engaged in the heart of the project: predicting heart failure. Employing machine learning models, we harnessed the power of grid search to optimize model parameters, meticulously fine-tuning algorithms to achieve the best predictive performance. Through an array of models including Logistic Regression, KNeighbors Classifier, DecisionTrees Classifier, Random Forest Classifier, Gradient Boosting Classifier, XGB Classifier, LGBM Classifier, and MLP Classifier, we harnessed metrics like accuracy, precision, recall, and F1-score to meticulously evaluate each model's efficacy. Venturing further into the realm of deep learning, we embarked on an exploration of neural networks, striving to capture intricate patterns in the data. Our arsenal included diverse architectures such as Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM) networks, Self Organizing Maps (SOMs), Recurrent Neural Networks (RNN), Deep Belief Networks (DBN), and Autoencoders. These architectures enabled us to unravel complex relationships within the data, yielding nuanced insights into the dynamics of heart failure prediction. Our approach to evaluating model performance was rigorous and thorough. By scrutinizing metrics such as accuracy, recall, precision, and F1-score, we gained a comprehensive understanding of the models' strengths and limitations. These metrics enabled us to make informed decisions about model selection and refinement, ensuring that our predictions were as accurate and reliable as possible. The evaluation phase emerges as a pivotal aspect, accentuated by an array of comprehensive metrics. Performance assessment encompasses metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Cross-validation and learning curves are strategically employed to mitigate overfitting and ensure model generalization. Furthermore, visual aids such as ROC curves and confusion matrices provide a lucid depiction of the models' interplay between sensitivity and specificity. Complementing our advanced analytical endeavors, we also embarked on the creation of a Python GUI using PyQt. This intuitive graphical interface provided an accessible platform for users to interact with the developed models and gain meaningful insights into heart health. The GUI streamlined the prediction process, making it user-friendly and facilitating the application of our intricate models to real-world scenarios. In conclusion, the "Heart Failure Analysis and Prediction" data science workshop was a journey through the realms of data exploration, feature distribution analysis, and the application of cutting-edge machine learning and deep learning techniques. By meticulously evaluating model performance, harnessing the capabilities of neural networks, and culminating in the creation of a user-friendly Python GUI, we armed participants with a comprehensive toolkit to analyze and predict heart failure with precision and innovation.


DATA SCIENCE WORKSHOP: Heart Failure Analysis and Prediction Using Scikit-Learn, Keras, and TensorFlow with Python GUI Related Books

DATA SCIENCE WORKSHOP: Heart Failure Analysis and Prediction Using Scikit-Learn, Keras, and TensorFlow with Python GUI
Language: en
Pages: 398
Authors: Vivian Siahaan
Categories: Computers
Type: BOOK - Published: 2023-08-18 - Publisher: BALIGE PUBLISHING

DOWNLOAD EBOOK

In this "Heart Failure Analysis and Prediction" data science workshop, we embarked on a comprehensive journey through the intricacies of cardiovascular health a
The Applied Data Science Workshop On Medical Datasets Using Machine Learning and Deep Learning with Python GUI
Language: en
Pages: 1574
Authors: Vivian Siahaan
Categories: Computers
Type: BOOK - Published: - Publisher: BALIGE PUBLISHING

DOWNLOAD EBOOK

Workshop 1: Heart Failure Analysis and Prediction Using Scikit-Learn, Keras, and TensorFlow with Python GUI Cardiovascular diseases (CVDs) are the number 1 caus
Text Analytics with Python
Language: en
Pages: 397
Authors: Dipanjan Sarkar
Categories: Computers
Type: BOOK - Published: 2016-11-30 - Publisher: Apress

DOWNLOAD EBOOK

Derive useful insights from your data using Python. You will learn both basic and advanced concepts, including text and language syntax, structure, and semantic
Data Analytics and Applications of the Wearable Sensors in Healthcare
Language: en
Pages: 498
Authors: Shabbir Syed-Abdul
Categories: Medical
Type: BOOK - Published: 2020-06-17 - Publisher: MDPI

DOWNLOAD EBOOK

This book provides a collection of comprehensive research articles on data analytics and applications of wearable devices in healthcare. This Special Issue pres
Automated Machine Learning
Language: en
Pages: 223
Authors: Frank Hutter
Categories: Computers
Type: BOOK - Published: 2019-05-17 - Publisher: Springer

DOWNLOAD EBOOK

This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing sys