Practical Data Science Programming for Medical Datasets Analysis and Prediction with Python GUI

Practical Data Science Programming for Medical Datasets Analysis and Prediction with Python GUI



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English | August 4, 2021 | ASIN: B09BZ6RW66 | 678 pages | True EPUB | 12.18 Mb

In this book, you will implement two data science projects using Scikit-Learn, Scipy, and other libraries with Python GUI.

In chapter 1, you will learn how to use Scikit-Learn, SVM, NumPy, Pandas, and other libraries to perform how to predict early stage diabetes using Early Stage Diabetes Risk Prediction Dataset. This dataset contains the sign and symptom data of newly diabetic or would be diabetic patient. This has been collected using direct questionnaires from the patients of Sylhet Diabetes Hospital in Sylhet, Bangladesh and approved by a doctor. The dataset consist of total 15 features and one target variable named class. Age: Age in years ranging from (20years to 65 years); Gender: Male / Female; Polyuria: Yes / No; Polydipsia: Yes/ No; Sudden weight loss: Yes/ No; Weakness: Yes/ No; Polyphagia: Yes/ No; Genital Thrush: Yes/ No; Visual blurring: Yes/ No; Itching: Yes/ No; Irritability: Yes/No; Delayed healing: Yes/ No; Partial Paresis: Yes/ No; Muscle stiffness: yes/ No; Alopecia: Yes/ No; Obesity: Yes/ No; This dataset contains the sign and symptpom data of newly diabetic or would be diabetic patient. This has been collected using direct questionnaires from the patients of Sylhet Diabetes Hospital in Sylhet, Bangladesh and approved by a doctor. You will develop a GUI using PyQt5 to plot distribution of features, feature importance, cross validation score, and prediced values versus true values. The machine learning models used in this project are Adaboost, Random Forest, Gradient Boosting, Logistic Regression, and Support Vector Machine.