DATA SCIENCE CRASH COURSE: Thyroid Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download DATA SCIENCE CRASH COURSE: Thyroid Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI PDF full book. Access full book title DATA SCIENCE CRASH COURSE: Thyroid Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI by Vivian Siahaan. Download full books in PDF and EPUB format.
Author: Vivian Siahaan
Publisher: BALIGE PUBLISHING
ISBN:
Category : Computers
Languages : en
Pages : 412
Get Book Here
Book Description
Thyroid disease is a prevalent condition that affects the thyroid gland, leading to various health issues. In this session of the Data Science Crash Course, we will explore the classification and prediction of thyroid disease using machine learning and deep learning techniques, all implemented with the power of Python and a user-friendly GUI built with PyQt. We will start by conducting data exploration on a comprehensive dataset containing relevant features and thyroid disease labels. Through analysis and pattern recognition, we will gain insights into the underlying factors contributing to thyroid disease. Next, we will delve into the machine learning phase, where we will implement popular algorithms including Support Vector, Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Gradient Boosting, Light Gradient Boosting, Naive Bayes, Adaboost, Extreme Gradient Boosting, and Multi-Layer Perceptron. These models will be trained using different preprocessing techniques, including raw data, normalization, and standardization, to evaluate their performance and accuracy. 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. Moving beyond traditional machine learning, we will build an artificial neural network (ANN) using TensorFlow. This ANN will capture complex relationships within the data and provide accurate predictions of thyroid disease. To ensure the effectiveness of our ANN, we will train it using a curated dataset split into training and testing sets. This will allow us to evaluate the model's performance and its ability to generalize predictions. To provide an interactive and user-friendly experience, we will develop a Graphical User Interface (GUI) using PyQt. The GUI will allow users to input data, select prediction methods (machine learning or deep learning), and visualize the results. Through the GUI, users can explore different prediction methods, compare performance, and gain insights into thyroid disease classification. Visualizations of training and validation loss, accuracy, and confusion matrices will enhance understanding and model evaluation. Line plots comparing true values and predicted values will further aid interpretation and insights into classification outcomes. Throughout the project, we will emphasize the importance of preprocessing techniques, feature selection, and model evaluation in building reliable and effective thyroid disease classification and prediction models. By the end of the project, readers will have gained practical knowledge in data exploration, machine learning, deep learning, and GUI development. They will be equipped to apply these techniques to other domains and real-world challenges. The project’s comprehensive approach, from data exploration to model development and GUI implementation, ensures a holistic understanding of thyroid disease classification and prediction. It empowers readers to explore applications of data science in healthcare and beyond. The combination of machine learning and deep learning techniques, coupled with the intuitive GUI, offers a powerful framework for thyroid disease classification and prediction. This project serves as a stepping stone for readers to contribute to the field of medical data science. Data-driven approaches in healthcare have the potential to unlock valuable insights and improve outcomes. The focus on thyroid disease classification and prediction in this session showcases the transformative impact of data science in the medical field. Together, let us embark on this journey to advance our understanding of thyroid disease and make a difference in the lives of individuals affected by this condition. Welcome to the Data Science Crash Course on Thyroid Disease Classification and Prediction!
Author: Vivian Siahaan
Publisher: BALIGE PUBLISHING
ISBN:
Category : Computers
Languages : en
Pages : 412
Get Book Here
Book Description
Thyroid disease is a prevalent condition that affects the thyroid gland, leading to various health issues. In this session of the Data Science Crash Course, we will explore the classification and prediction of thyroid disease using machine learning and deep learning techniques, all implemented with the power of Python and a user-friendly GUI built with PyQt. We will start by conducting data exploration on a comprehensive dataset containing relevant features and thyroid disease labels. Through analysis and pattern recognition, we will gain insights into the underlying factors contributing to thyroid disease. Next, we will delve into the machine learning phase, where we will implement popular algorithms including Support Vector, Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Gradient Boosting, Light Gradient Boosting, Naive Bayes, Adaboost, Extreme Gradient Boosting, and Multi-Layer Perceptron. These models will be trained using different preprocessing techniques, including raw data, normalization, and standardization, to evaluate their performance and accuracy. 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. Moving beyond traditional machine learning, we will build an artificial neural network (ANN) using TensorFlow. This ANN will capture complex relationships within the data and provide accurate predictions of thyroid disease. To ensure the effectiveness of our ANN, we will train it using a curated dataset split into training and testing sets. This will allow us to evaluate the model's performance and its ability to generalize predictions. To provide an interactive and user-friendly experience, we will develop a Graphical User Interface (GUI) using PyQt. The GUI will allow users to input data, select prediction methods (machine learning or deep learning), and visualize the results. Through the GUI, users can explore different prediction methods, compare performance, and gain insights into thyroid disease classification. Visualizations of training and validation loss, accuracy, and confusion matrices will enhance understanding and model evaluation. Line plots comparing true values and predicted values will further aid interpretation and insights into classification outcomes. Throughout the project, we will emphasize the importance of preprocessing techniques, feature selection, and model evaluation in building reliable and effective thyroid disease classification and prediction models. By the end of the project, readers will have gained practical knowledge in data exploration, machine learning, deep learning, and GUI development. They will be equipped to apply these techniques to other domains and real-world challenges. The project’s comprehensive approach, from data exploration to model development and GUI implementation, ensures a holistic understanding of thyroid disease classification and prediction. It empowers readers to explore applications of data science in healthcare and beyond. The combination of machine learning and deep learning techniques, coupled with the intuitive GUI, offers a powerful framework for thyroid disease classification and prediction. This project serves as a stepping stone for readers to contribute to the field of medical data science. Data-driven approaches in healthcare have the potential to unlock valuable insights and improve outcomes. The focus on thyroid disease classification and prediction in this session showcases the transformative impact of data science in the medical field. Together, let us embark on this journey to advance our understanding of thyroid disease and make a difference in the lives of individuals affected by this condition. Welcome to the Data Science Crash Course on Thyroid Disease Classification and Prediction!
Author: Vivian Siahaan
Publisher: BALIGE PUBLISHING
ISBN:
Category : Computers
Languages : en
Pages : 917
Get Book Here
Book Description
PROJECT 1: THE APPLIED DATA SCIENCE WORKSHOP: Prostate Cancer Classification and Recognition Using Machine Learning and Deep Learning with Python GUI Prostate cancer is cancer that occurs in the prostate. The prostate is a small walnut-shaped gland in males that produces the seminal fluid that nourishes and transports sperm. Prostate cancer is one of the most common types of cancer. Many prostate cancers grow slowly and are confined to the prostate gland, where they may not cause serious harm. However, while some types of prostate cancer grow slowly and may need minimal or even no treatment, other types are aggressive and can spread quickly. The dataset used in this project consists of 100 patients which can be used to implement the machine learning and deep learning algorithms. The dataset consists of 100 observations and 10 variables (out of which 8 numeric variables and one categorical variable and is ID) which are as follows: Id, Radius, Texture, Perimeter, Area, Smoothness, Compactness, Diagnosis Result, Symmetry, and Fractal Dimension. The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 2: THE APPLIED DATA SCIENCE WORKSHOP: Urinary Biomarkers Based Pancreatic Cancer Classification and Prediction Using Machine Learning with Python GUI Pancreatic cancer is an extremely deadly type of cancer. Once diagnosed, the five-year survival rate is less than 10%. However, if pancreatic cancer is caught early, the odds of surviving are much better. Unfortunately, many cases of pancreatic cancer show no symptoms until the cancer has spread throughout the body. A diagnostic test to identify people with pancreatic cancer could be enormously helpful. In a paper by Silvana Debernardi and colleagues, published this year in the journal PLOS Medicine, a multi-national team of researchers sought to develop an accurate diagnostic test for the most common type of pancreatic cancer, called pancreatic ductal adenocarcinoma or PDAC. They gathered a series of biomarkers from the urine of three groups of patients: Healthy controls, Patients with non-cancerous pancreatic conditions, like chronic pancreatitis, and Patients with pancreatic ductal adenocarcinoma. When possible, these patients were age- and sex-matched. The goal was to develop an accurate way to identify patients with pancreatic cancer. The key features are four urinary biomarkers: creatinine, LYVE1, REG1B, and TFF1. Creatinine is a protein that is often used as an indicator of kidney function. YVLE1 is lymphatic vessel endothelial hyaluronan receptor 1, a protein that may play a role in tumor metastasis. REG1B is a protein that may be associated with pancreas regeneration. TFF1 is trefoil factor 1, which may be related to regeneration and repair of the urinary tract. The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, and MLP classifier. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 3: DATA SCIENCE CRASH COURSE: Voice Based Gender Classification and Prediction Using Machine Learning and Deep Learning with Python GUI This dataset was created to identify a voice as male or female, based upon acoustic properties of the voice and speech. The dataset consists of 3,168 recorded voice samples, collected from male and female speakers. The voice samples are pre-processed by acoustic analysis in R using the seewave and tuneR packages, with an analyzed frequency range of 0hz-280hz (human vocal range). The following acoustic properties of each voice are measured and included within the CSV: meanfreq: mean frequency (in kHz); sd: standard deviation of frequency; median: median frequency (in kHz); Q25: first quantile (in kHz); Q75: third quantile (in kHz); IQR: interquantile range (in kHz); skew: skewness; kurt: kurtosis; sp.ent: spectral entropy; sfm: spectral flatness; mode: mode frequency; centroid: frequency centroid (see specprop); peakf: peak frequency (frequency with highest energy); meanfun: average of fundamental frequency measured across acoustic signal; minfun: minimum fundamental frequency measured across acoustic signal; maxfun: maximum fundamental frequency measured across acoustic signal; meandom: average of dominant frequency measured across acoustic signal; mindom: minimum of dominant frequency measured across acoustic signal; maxdom: maximum of dominant frequency measured across acoustic signal; dfrange: range of dominant frequency measured across acoustic signal; modindx: modulation index. Calculated as the accumulated absolute difference between adjacent measurements of fundamental frequencies divided by the frequency range; and label: male or female. The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 4: DATA SCIENCE CRASH COURSE: Thyroid Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI Thyroid disease is a general term for a medical condition that keeps your thyroid from making the right amount of hormones. Thyroid typically makes hormones that keep body functioning normally. When the thyroid makes too much thyroid hormone, body uses energy too quickly. The two main types of thyroid disease are hypothyroidism and hyperthyroidism. Both conditions can be caused by other diseases that impact the way the thyroid gland works. Dataset used in this project was from Garavan Institute Documentation as given by Ross Quinlan 6 databases from the Garavan Institute in Sydney, Australia. Approximately the following for each database: 2800 training (data) instances and 972 test instances. This dataset contains plenty of missing data, while 29 or so attributes, either Boolean or continuously-valued. The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, MLP classifier, and CNN 1D. Finally, you will develop a GUI using PyQt5 to plot boundary decision, ROC, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy.
Author: Jason Brownlee
Publisher: Machine Learning Mastery
ISBN:
Category : Computers
Languages : en
Pages : 266
Get Book Here
Book Description
Deep learning is the most interesting and powerful machine learning technique right now. Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. Tap into their power in a few lines of code using Keras, the best-of-breed applied deep learning library. In this Ebook, learn exactly how to get started and apply deep learning to your own machine learning projects.
Author: Benjamin S. Baumer
Publisher: CRC Press
ISBN: 0429575394
Category : Business & Economics
Languages : en
Pages : 830
Get Book Here
Book Description
From a review of the first edition: "Modern Data Science with R... is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics" (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions. The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice.
Author: Jason Brownlee
Publisher: Machine Learning Mastery
ISBN:
Category : Computers
Languages : en
Pages : 247
Get Book Here
Book Description
Machine learning is not just for professors. Weka is a top machine learning platform that provides an easy-to-use graphical interface and state-of-the-art algorithms. In this Ebook, learn exactly how to get started with applied machine learning using the Weka platform.
Author: David Edward Goldberg
Publisher: Addison-Wesley Professional
ISBN:
Category : Computers
Languages : en
Pages : 436
Get Book Here
Book Description
A gentle introduction to genetic algorithms. Genetic algorithms revisited: mathematical foundations. Computer implementation of a genetic algorithm. Some applications of genetic algorithms. Advanced operators and techniques in genetic search. Introduction to genetics-based machine learning. Applications of genetics-based machine learning. A look back, a glance ahead. A review of combinatorics and elementary probability. Pascal with random number generation for fortran, basic, and cobol programmers. A simple genetic algorithm (SGA) in pascal. A simple classifier system(SCS) in pascal. Partition coefficient transforms for problem-coding analysis.
Author: Bryan P. Bergeron
Publisher: Prentice Hall Professional
ISBN: 9780131008250
Category : Computers
Languages : en
Pages : 472
Get Book Here
Book Description
Comprehensive and concise, this handbook has chapters on computing visualization, large database designs, advanced pattern matching and other key bioinformatics techniques. It is a practical guide to computing in the growing field of Bioinformatics--the study of how information is represented and transmitted in biological systems, starting at the molecular level.
Author: Ashish Kumar Luhach
Publisher:
ISBN: 9789811501098
Category : Computer science
Languages : en
Pages : 477
Get Book Here
Book Description
This two-volume set (CCIS 1075 and CCIS 1076) constitutes the refereed proceedings of the Third International Conference on Advanced Informatics for Computing Research, ICAICR 2019, held in Shimla, India, in June 2019. The 78 revised full papers presented were carefully reviewed and selected from 382 submissions. The papers are organized in topical sections on computing methodologies; hardware; information systems; networks; software and its engineering.
Author: Andreas Holzinger
Publisher: Springer Nature
ISBN: 3030504026
Category : Computers
Languages : en
Pages : 351
Get Book Here
Book Description
Data driven Artificial Intelligence (AI) and Machine Learning (ML) in digital pathology, radiology, and dermatology is very promising. In specific cases, for example, Deep Learning (DL), even exceeding human performance. However, in the context of medicine it is important for a human expert to verify the outcome. Consequently, there is a need for transparency and re-traceability of state-of-the-art solutions to make them usable for ethical responsible medical decision support. Moreover, big data is required for training, covering a wide spectrum of a variety of human diseases in different organ systems. These data sets must meet top-quality and regulatory criteria and must be well annotated for ML at patient-, sample-, and image-level. Here biobanks play a central and future role in providing large collections of high-quality, well-annotated samples and data. The main challenges are finding biobanks containing ‘‘fit-for-purpose’’ samples, providing quality related meta-data, gaining access to standardized medical data and annotations, and mass scanning of whole slides including efficient data management solutions.
Author: Anna Esposito
Publisher: Springer Nature
ISBN: 9811389500
Category : Technology & Engineering
Languages : en
Pages : 523
Get Book Here
Book Description
The book presents research that contributes to the development of intelligent dialog systems to simplify diverse aspects of everyday life, such as medical diagnosis and entertainment. Covering major thematic areas: machine learning and artificial neural networks; algorithms and models; and social and biometric data for applications in human–computer interfaces, it discusses processing of audio-visual signals for the detection of user-perceived states, the latest scientific discoveries in processing verbal (lexicon, syntax, and pragmatics), auditory (voice, intonation, vocal expressions) and visual signals (gestures, body language, facial expressions), as well as algorithms for detecting communication disorders, remote health-status monitoring, sentiment and affect analysis, social behaviors and engagement. Further, it examines neural and machine learning algorithms for the implementation of advanced telecommunication systems, communication with people with special needs, emotion modulation by computer contents, advanced sensors for tracking changes in real-life and automatic systems, as well as the development of advanced human–computer interfaces. The book does not focus on solving a particular problem, but instead describes the results of research that has positive effects in different fields and applications.
