Analisis Dan Prediksi Stroke Menggunakan Scikit Learn Keras Dan Tensorflow Dengan Python Gui

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Analisis Dan Prediksi Stroke Menggunakan Scikit Learn Keras Dan Tensorflow Dengan Python Gui

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Author : Vivian Siahaan
language : id
Publisher: BALIGE PUBLISHING
Release Date : 2021-09-09

Analisis Dan Prediksi Stroke Menggunakan Scikit Learn Keras Dan Tensorflow Dengan Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-09 with Computers categories.

Menurut Organisasi Kesehatan Dunia (WHO), stroke adalah penyebab kematian ke-2 secara global, yang bertanggung jawab atas sekitar 11% dari total kematian. Dataset yang digunakan pada penelitian ini berguna untuk memprediksi kemungkinan seorang pasien terkena stroke berdasarkan parameter masukan seperti jenis kelamin, usia, berbagai penyakit, dan status merokok. Setiap baris dalam data memberikan informasi yang relevan tentang pasien. Informasi tiap kolom: id: Pengenal unik; gender: "Male", "Female" atau "Other"; age: Usia pasien; hypertension: 0 jika pasien tidak memiliki hipertensi, 1 jika pasien memiliki hipertensi; heart_disease: 0 jika pasien tidak memiliki penyakit jantung, 1 jika pasien memiliki penyakit jantung; ever_married: "No" atau "Yes"; work_type: "children", "Govt_jov", "Never_worked", "Private" atau "Self-employed"; Residence_type: "Rural" atau "Urban"; avg_glucose_level: Rata-rata kadar glukosa dalam darah; bmi: body mass index; smoking_status: "formerly smoked", "never smoked", "smokes" atau "Unknown"*; stroke: 1 jika pasien mengalami stroke atau 0 jika tidak. Selanjutnya, Anda akan belajar menggunakan Scikit-Learn, Keras, TensorFlow, NumPy, Pandas, Seaborn, dan sejumlah Pustaka lain untuk menganalisa dan memprediksi stroke menggunakan dataset yang disediakan di Kaggle. Model-model yang digunakan adalah K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, Gradient Boosting, LGBM classifier, XGB classifier, MLP classifier, dan CNN 1D. Terakhir, Anda akan mengembangkan GUI menggunakan Qt Designer dan PyQt5 untuk ROC, distribusi fitur, keutamaan fitur, menampilkan batas-batas keputusan tiap model, diagram nilai-nilai prediksi versus nilai-nilai sebenarnya, matriks confusion, rugi pelatihan, rugi akurasi, kurva pembelajaran model, skalabilitas model, dan kinerja model.

Ai Dan Data Science Dengan Python Gui Studi Kasus Covid 19 Dan Stroke

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Author : Vivian Siahaan
language : id
Publisher: BALIGE PUBLISHING
Release Date : 2021-10-08

Ai Dan Data Science Dengan Python Gui Studi Kasus Covid 19 Dan Stroke written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-08 with Computers categories.

KASUS 1: COVID-19 Karena penyebaran COVID-19, pengembangan vaksin dituntut sesegera mungkin. Terlepas dari pentingnya analisis data dalam pengembangan vaksin, tidak banyak dataset sederhana yang dapat ditangani oleh pada analis data. Kumpulan data dan kode sampel telah dikumpulkan untuk prediksi epitop Bcell, salah satu topik penelitian utama dalam pengembangan vaksin, tersedia secara gratis. Dataset ini dikembangkan selama proses penelitian kami dan data yang terkandung di dalamnya diperoleh dari IEDB dan UniProt. Sel B yang menginduksi respon imun spesifik antigen in vivo menghasilkan sejumlah besar antibodi spesifik antigen dengan mengenali subregion (wilayah epitop) protein antigen. Sel B ini dapat menghambat fungsinya dengan mengikat antibodi ke protein antigen. Memprediksi daerah epitop bermanfaat untuk desain dan pengembangan vaksin yang bertujuan untuk menginduksi produksi antibodi spesifik antigen. Sel B inilah menjadi dataset utama yang dipakai pada proyek ini. Dataset ini memuat kolom: parent_protein_id, protein_seq, start_position, end_position, peptide_seq, chou_fasman, emini, kolaskar_tongaonkar, parker, hydrophobicity, isoelectric_point, aromacity, stability, dan target. Selanjutnya, Anda akan belajar menggunakan Scikit-Learn, Keras, TensorFlow, NumPy, Pandas, Seaborn, dan sejumlah Pustaka lain untuk memprediksi COVID-19 Epitope menggunakan dataset COVID-19/SARS B-cell Epitope Prediction yang disediakan di Kaggle. Model-model machine learning yang digunakan adalah K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, Gradient Boosting, XGB classifier, dan MLP classifier. Kemudian, Anda akan mempelajari cara menerapkan model CNN sekuensial dan VGG16 untuk mendeteksi dan memprediksi Covid-19 X-RAY menggunakan COVID-19 Xray Dataset (Train & Test Sets) yang disediakan di Kaggle. Folder itu sendiri terdiri dari dua subfolder: test dan train. Terakhir, Anda akan mengembangkan GUI menggunakan PyQt5 untuk menampilkan batas-batas keputusan tiap model, ROC, distribusi fitur, keutamaan fitur, skor validasi silang, nilai-nilai prediksi versus nilai-nilai sebenarnya, matriks confusion, rugi pelatihan, dan rugi akurasi. KASUS 2: STROKE Menurut Organisasi Kesehatan Dunia (WHO), stroke adalah penyebab kematian ke-2 secara global, yang bertanggung jawab atas sekitar 11% dari total kematian. Dataset yang digunakan pada penelitian ini berguna untuk memprediksi kemungkinan seorang pasien terkena stroke berdasarkan parameter masukan seperti jenis kelamin, usia, berbagai penyakit, dan status merokok. Setiap baris dalam data memberikan informasi yang relevan tentang pasien. Informasi tiap kolom: id: Pengenal unik; gender: "Male", "Female" atau "Other"; age: Usia pasien; hypertension: 0 jika pasien tidak memiliki hipertensi, 1 jika pasien memiliki hipertensi; heart_disease: 0 jika pasien tidak memiliki penyakit jantung, 1 jika pasien memiliki penyakit jantung; ever_married: "No" atau "Yes"; work_type: "children", "Govt_jov", "Never_worked", "Private" atau "Self-employed"; Residence_type: "Rural" atau "Urban"; avg_glucose_level: Rata-rata kadar glukosa dalam darah; bmi: body mass index; smoking_status: "formerly smoked", "never smoked", "smokes" atau "Unknown"*; stroke: 1 jika pasien mengalami stroke atau 0 jika tidak. Selanjutnya, Anda akan belajar menggunakan Scikit-Learn, Keras, TensorFlow, NumPy, Pandas, Seaborn, dan sejumlah Pustaka lain untuk menganalisa dan memprediksi stroke menggunakan dataset yang disediakan di Kaggle. Model-model yang digunakan adalah K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, Gradient Boosting, LGBM classifier, XGB classifier, MLP classifier, dan CNN 1D. Terakhir, Anda akan mengembangkan GUI menggunakan Qt Designer dan PyQt5 untuk ROC, distribusi fitur, keutamaan fitur, menampilkan batas-batas keputusan tiap model, diagram nilai-nilai prediksi versus nilai-nilai sebenarnya, matriks confusion, rugi pelatihan, rugi akurasi, kurva pembelajaran model, skalabilitas model, dan kinerja model.

Advancing Information Systems Theories

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Author : Nik Rushdi Hassan
language : en
Publisher: Springer Nature
Release Date : 2021-03-21

Advancing Information Systems Theories written by Nik Rushdi Hassan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-21 with Computers categories.

The information systems (IS) field represents a multidisciplinary area that links the rapidly changing technology of information (or communications and information technology, ICT) to the business and social environment. Despite the potential that the IS field has to develop its own native theories to address current issues involving ICT it has consistently borrowed theories from its “reference disciplines,” often uncritically, to legitimize its research. This volume is the first of a series intended to advance IS research beyond this form of borrowed legitimization and derivative research towards fresh and original research that naturally comes from its own theories. It is inconceivable for a field so relevant to the era of the hyper-connected society, disruptive technologies, big data, social media, "fake news" and the weaponization of information to not be brimming with its own theories. The first step in reaching the goal of developing native IS theories is to reach an agreement on the need for theory (its rationale) and its role as the most distinctive product of human intellectual activity. This volume addresses what theories are, why bother with theories and the process of theorizing itself because the process of developing theories cannot be divorced from the product of that process. It will lay out a research agenda for decades to come and will be invaluable reading for any academic in the IS field and related disciplines concerned with information, systems, technology and their management.

Stroke Analysis And Prediction Using Scikit Learn Keras And Tensorflow With Python Gui

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-07-15

Stroke Analysis And Prediction Using Scikit Learn Keras And Tensorflow With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-15 with Computers categories.

In this project, we will perform an analysis and prediction task on stroke data using machine learning and deep learning techniques. The entire process will be implemented with Python GUI for a user-friendly experience. We start by exploring the stroke dataset, which contains information about various factors related to individuals and their likelihood of experiencing a stroke. We load the dataset and examine its structure, features, and statistical summary. Next, we preprocess the data to ensure its suitability for training machine learning models. This involves handling missing values, encoding categorical variables, and scaling numerical features. We utilize techniques such as data imputation and label encoding. To gain insights from the data, we visualize its distribution and relationships between variables. We create plots such as histograms, scatter plots, and correlation matrices to understand the patterns and correlations in the data. To improve model performance and reduce dimensionality, we select the most relevant features for prediction. We employ techniques such as correlation analysis, feature importance ranking, and domain knowledge to identify the key predictors of stroke. Before training our models, we split the dataset into training and testing subsets. The training set will be used to train the models, while the testing set will evaluate their performance on unseen data. We construct several machine learning models to predict stroke. These models include Support Vector, Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Gradient Boosting, Light Gradient Boosting, Naive Bayes, Adaboost, and XGBoost. Each model is built and trained using the training dataset. We train each model on the training dataset and evaluate its performance using appropriate metrics such as accuracy, precision, recall, and F1-score. This helps us assess how well the models can predict stroke based on the given features. To optimize the models' performance, we perform hyperparameter tuning using techniques like grid search or randomized search. This involves systematically exploring different combinations of hyperparameters to find the best configuration for each model. After training and tuning the models, we save them to disk using joblib. This allows us to reuse the trained models for future predictions without having to train them again. With the models trained and saved, we move on to implementing the Python GUI. We utilize PyQt libraries to create an interactive graphical user interface that provides a seamless user experience. The GUI consists of various components such as buttons, checkboxes, input fields, and plots. These components allow users to interact with the application, select prediction models, and visualize the results. In addition to the machine learning models, we also implement an ANN using TensorFlow. The ANN is trained on the preprocessed dataset, and its architecture consists of a dense layer with a sigmoid activation function. We train the ANN on the training dataset, monitoring its performance using metrics like loss and accuracy. We visualize the training progress by plotting the loss and accuracy curves over epochs. Once the ANN is trained, we save the model to disk using the h5 format. This allows us to load the trained ANN for future predictions. In the GUI, users have the option to choose the ANN as the prediction model. When selected, the ANN model is loaded from disk, and predictions are made on the testing dataset. The predicted labels are compared with the true labels for evaluation. To assess the accuracy of the ANN predictions, we calculate various evaluation metrics such as accuracy score, precision, recall, and classification report. These metrics provide insights into the ANN's performance in predicting stroke. We create plots to visualize the results of the ANN predictions. These plots include a comparison of the true values and predicted values, as well as a confusion matrix to analyze the classification accuracy. The training history of the ANN, including the loss and accuracy curves over epochs, is plotted and displayed in the GUI. This allows users to understand how the model's performance improved during training. In summary, this project covers the analysis and prediction of stroke using machine learning and deep learning models. It encompasses data exploration, preprocessing, model training, hyperparameter tuning, GUI implementation, ANN training, and prediction visualization. The Python GUI enhances the user experience by providing an interactive and intuitive platform for exploring and predicting stroke based on various features.

125 Problems In Text Algorithms

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Author : Maxime Crochemore
language : en
Publisher: Cambridge University Press
Release Date : 2021-07

125 Problems In Text Algorithms written by Maxime Crochemore and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07 with Computers categories.

Worked problems offer an interesting way to learn and practice with key concepts of string algorithms and combinatorics on words.

A Hands On Introduction To Data Science

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Author : Chirag Shah
language : en
Publisher: Cambridge University Press
Release Date : 2020-04-02

A Hands On Introduction To Data Science written by Chirag Shah and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-02 with Business & Economics categories.

An introductory textbook offering a low barrier entry to data science; the hands-on approach will appeal to students from a range of disciplines.

A History Of Communication Technology

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Author : Philip Loubere
language : en
Publisher: Routledge
Release Date : 2021-04-11

A History Of Communication Technology written by Philip Loubere and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-11 with History categories.

This book is a comprehensive illustrated account of the technologies and inventions in mass communication that have accelerated the advancement of human culture and society. A History of Communication Technology covers a timeline in the history of mass communication that begins with human prehistory and extends all the way to the current digital age. Using rich, full-color graphics and diagrams, the book details the workings of various mass communication inventions, from paper-making, printing presses, photography, radio, TV, film, and video, to computers, digital devices, and the Internet. Readers are given insightful narratives on the social impact of these technologies, brief historical accounts of the inventors, and sidebars on the related technologies that enabled these inventions. This book is ideal for students in introductory mass communication, visual communication, and history of media courses, offering a highly approachable, graphic-oriented approach to the history of communication technologies. Additional digital resources for the book are available at https://comtechhistory.site/

Advanced Digital Image Processing And Its Applications In Big Data

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Author : Ankur Dumka
language : en
Publisher: CRC Press
Release Date : 2020-12-09

Advanced Digital Image Processing And Its Applications In Big Data written by Ankur Dumka and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-09 with Computers categories.

This book covers the technology of digital image processing in various fields with big data and their applications. Readers will understand various technologies and strategies used in digital image processing as well as handling big data, using machine-learning techniques. This book will help to improve the skills of students and researchers in such fields as engineering, agriculture, and medical imaging. There is a need to be able to understand and analyse the latest developments of digital image technology. As such, this book will cover: · Applications such as biomedical science and biometric image processing, content-based image retrieval, remote sensing, pattern recognition, shape and texture analysis · New concepts in color interpolation to produce the full color from the sub-pattern bare pattern color prevalent in today's digital cameras and other imaging devices · Image compression standards that are needed to serve diverse applications · Applications of remote sensing, medical science, traffic management, education, innovation, and analysis in agricultural design and image processing · Both soft and hard computing approaches at great length in relation to major image processing tasks · The direction and development of current and future research in many areas of image processing · A comprehensive bibliography for additional research (integrated within the framework of the book) This book focuses not only on theoretical and practical knowledge in the field but also on the traditional and latest tools and techniques adopted in image processing and data science. It also provides an indispensable guide to a wide range of basic and advanced techniques in the fields of image processing and data science.

A History Of Data Visualization And Graphic Communication

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Author : Michael Friendly
language : en
Publisher: Harvard University Press
Release Date : 2021-06-08

A History Of Data Visualization And Graphic Communication written by Michael Friendly and has been published by Harvard University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-08 with Science categories.

A comprehensive history of data visualizationÑits origins, rise, and effects on the ways we think about and solve problems. With complex information everywhere, graphics have become indispensable to our daily lives. Navigation apps show real-time, interactive traffic data. A color-coded map of exit polls details election balloting down to the county level. Charts communicate stock market trends, government spending, and the dangers of epidemics. A History of Data Visualization and Graphic Communication tells the story of how graphics left the exclusive confines of scientific research and became ubiquitous. As data visualization spread, it changed the way we think. Michael Friendly and Howard Wainer take us back to the beginnings of graphic communication in the mid-seventeenth century, when the Dutch cartographer Michael Florent van Langren created the first chart of statistical data, which showed estimates of the distance from Rome to Toledo. By 1786 William Playfair had invented the line graph and bar chart to explain trade imports and exports. In the nineteenth century, the Ògolden ageÓ of data display, graphics found new uses in tracking disease outbreaks and understanding social issues. Friendly and Wainer make the case that the explosion in graphical communication both reinforced and was advanced by a cognitive revolution: visual thinking. Across disciplines, people realized that information could be conveyed more effectively by visual displays than by words or tables of numbers. Through stories and illustrations, A History of Data Visualization and Graphic Communication details the 400-year evolution of an intellectual framework that has become essential to both science and society at large.

97 Things Every Ux Practitioner Should Know

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Author : Tom Greever
language : en
Publisher: O'Reilly Media
Release Date : 2021-07-20

97 Things Every Ux Practitioner Should Know written by Tom Greever and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-20 with Business & Economics categories.

Tap into the wisdom of experts to learn what every UX designer needs to know. With 97 short and extremely useful tips, you'll discover new approaches to old problems, pick up road-tested best practices, and hone your skills through sound advice. Working in UX involves much more than just creating user interfaces. UX teams struggle with understanding what's important, which practices they should know deeply, or what approaches aren't helpful at all. With these 97 concise tips, editor Tom Greever presents a wealth of advice and knowledge from experts who have practiced UX throughout their careers. "Make a Commercial First"--Steve Turbek "Think Twice Before You Specialize"--Ingrid Cruz "You Have the Power to Design the Future"--Gail Giacobbe "Design the Presentation That Gets the Job Done"--Rakesh Patwari "Bring Themes, Not Interview Questions to Exploratory Research"--Shanti Kanhai "Not Everything That Counts Can Be Counted"--Drew Lepp "Design with Diversity in Mind"--Caleb Williams "There Is No Standard Design Process"--Luke Chen