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.

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.

Covid 19 Analisis Klasifikasi Dan Deteksi Menggunakan Scikit Learn Keras Dan Tensorflow Dengan Python Gui

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2021-09-02

Covid 19 Analisis Klasifikasi Dan Deteksi 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 2021-09-02 with Computers categories.

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 menggunakan data science. 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 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 deep learning, 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.

Pemrograman Data Science Studi Kasus Klasifikasi Dan Prediksi Hepatitis C Menggunakan Scikit Learn Keras Dan Tensorflow Dengan Python Gui

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Author : Vivian Siahaan
language : id
Publisher: BALIGE PUBLISHING
Release Date : 2021-10-18

Pemrograman Data Science Studi Kasus Klasifikasi Dan Prediksi Hepatitis C 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 2021-10-18 with Computers categories.

Dataset yang dipakai pada buku ini berisi nilai-nilai laboratorium dari sejumlah donor darah dan pasien Hepatitis C dan nilai-nilai demografis seperti usia dan lainnya. Dataset diperoleh dari UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/HCV+data. Semua atribut kecuali Category dan Sex adalah numerikal. Atribut 1 sampai 4 mengacu pada data pasien dan atribut 5 sampai 14 mengacu pada data laboratorium: X (Patient ID/No.), Category (diagnosis) (values: '0=Blood Donor', '0s=suspect Blood Donor', '1=Hepatitis', '2=Fibrosis', '3=Cirrhosis'), Age (in years), Sex (f,m), ALB, ALP, ALT, AST, BIL, CHE, CHOL, CREA, GGT, and PROT. Atribut target untuk klasifikasi adalah Category (2): blood donors vs. Hepatitis C (termasuk ('just' Hepatitis C, Fibrosis, Cirrhosis). Selanjutnya, pada buku ini Anda akan belajar menggunakan Scikit-Learn, Keras, TensorFlow, NumPy, Pandas, Seaborn, dan sejumlah Pustaka lain untuk mengklasifikasi dan memprediksi Hepatitis C. 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 ANN. 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, kurva rugi, kurva 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.

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.

Accounting Ethics Education

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Author : Margarida M. Pinheiro
language : en
Publisher: Routledge
Release Date : 2022-05

Accounting Ethics Education written by Margarida M. Pinheiro and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05 with Accountants categories.

The book promotes a comprehensive reflection around how ethics can and should be taught to accounting students, discussing and highlighting the most updated research on accounting ethics education, being an essential and useful reference in the field.

Advertising Management In A Digital Environment

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Author : Larry D. Kelley
language : en
Publisher: Routledge
Release Date : 2021-07-21

Advertising Management In A Digital Environment written by Larry D. Kelley and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-21 with Business & Economics categories.

Advertising Management in a Digital Environment: Text and Cases blends the latest methods for digital communication and an understanding of the global landscape with the best practices of the functional areas of management. Divided into three core sections, the book provides a truly holistic approach to Advertising Management. The first part considers the fundamentals of advertising management, including leadership, ethics and corporate social responsibility, and finance and budgeting. The second part considers human capital management and managing across cultures, whilst the third part discusses strategic planning, decision making and brand strategy. To demonstrate how theory translates to practice in advertising, each chapter is illustrated with real-life case studies from a broad range of sectors, and practical exercises allow case analysis and further learning. This new textbook offers an integrated and global approach to Advertising Management and should be core or recommended reading for undergraduate and postgraduate students of Media Management, Advertising, Marketing Management and Strategy, Communications and Public Relations. The applied approach provided by case study analysis makes it equally suitable for those in executive education and studying for professional qualifications.

A Guide To Islamic Asset Management

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Author : John A. Sandwick
language : en
Publisher: Edward Elgar Publishing
Release Date : 2021-03-26

A Guide To Islamic Asset Management written by John A. Sandwick and has been published by Edward Elgar Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-26 with Business & Economics categories.

This original book examines how investment theory and regulatory constraints are linked to the professional processes of portfolio investments, and how the principles of Islam as defined by sharia fit into these processes. It also explores the measures required to create and grow a global Islamic asset management industry.