This project is built upon a dataset originally sourced from the National Institute of Diabetes and Digestive and Kidney Diseases. The primary objective of this dataset is to diagnostically predict whether a patient has diabetes based on specific diagnostic measurements included in the dataset. To maintain data integrity, several constraints were applied during the selection of instances from a larger database. Notably, all patients included in this dataset are females who are at least 21 years old and of Pima Indian heritage.
The dataset comprises several columns, each with a specific medical measurement or attribute. Here's an overview of these columns:
-Pregnancies: The number of times the patient has been pregnant. -Glucose: The patient's glucose concentration in plasma. -Blood Pressure: The patient's diastolic blood pressure (mm Hg). -Skin Thickness: The patient's skin thickness (mm). -Insulin: The patient's insulin level (mu U/ml). -BMI (Body Mass Index): A measure of the patient's body mass relative to their height. -Diabetes Pedigree Function: A function that scores the patient's family history of diabetes. -Age: The patient's age (in years). -Outcome: The target variable indicating whether the patient has diabetes (1 for positive, 0 for negative).
In this project, I developed a web-based Diabetes Prediction System using Python and the Django web framework. The goal was to create a user-friendly application that allows individuals to input their medical data, and based on this data, predict whether they are likely to have diabetes.
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Data Preprocessing: I conducted extensive data preprocessing tasks, including handling missing values, scaling features, and encoding categorical variables.
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Machine Learning Model: I trained a machine learning model using the preprocessed data. The model, based on a classification algorithm, can predict diabetes outcomes accurately.
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Web Application: I designed and implemented a web application using Django. The app provides a user-friendly interface for users to input their medical data and receive predictions.
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User Experience: I focused on creating an intuitive and visually appealing user experience, with features such as form validation and result display.
To use the Diabetes Prediction System, simply visit the web application and fill out the provided form with your medical data. The system will process your information and provide you with a prediction regarding whether you are likely to have diabetes.
Python Django Machine Learning (Scikit-Learn) HTML/CSS Git & GitHub
This project showcases the application of machine learning and web development to solve real-world healthcare challenges. It empowers individuals to assess their risk of diabetes based on their medical data, promoting early detection and proactive healthcare management.