My Projects
E-Healthcare Shopping (Major project)
E-healthcare Shopping, also known as electronic healthcare Shopping, refers to the use of digital technologies and telecommunications to provide and improve healthcare services Medicines. It encompasses a wide range of applications, including telemedicine, electronic health records (EHRs), mobile health apps, wearable health devices, and online health information resources.
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Key Components of E-Healthcare:
1.User Interface (UI)
2. Product Catalogue
3. Search Functionality
4. User Authentication
5. Shopping Cart
6. Checkout Process
7. Order Management
8. User Profile Management
This project was developed for Customers for thier convenience and user friendly using HTML 5,CSS 3Javascript as Frontend,PHP as Backend, MySQL as Database and Apache as local Server.
HEART DISEASE PREDICTION USING MACHINE LEARNING(Mini project)
Heart disease prediction using machine learning involves utilizing various algorithms and data analysis techniques to identify patterns and risk factors associated with cardiovascular conditions.
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1.Data Collection :blood pressure, cholesterol levels, and electrocardiogram
2.Feature Selection: age, gender, family history, smoking status, physical activity, diet, and other health indicators.
3.Algorithm Selection:logistic regression, decision trees, random forests, support vector machines (SVM), k-nearest neighbors (KNN), and neural networks.
4.Model Training: model with input data (features) and the corresponding output. so the model can learn the relationship between them.
5.Model Validation and Testing: accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve.
6.Prediction and Analysis: help in early diagnosis and prompt intervention.
7.Implementation and Deployment: integrated into healthcare systems and improving patient outcomes through personalized treatment plans.
This project was developed for Health providers for thier convenience and user friendly using FLASK 3.0 as Frontend,PYTHON (Machine learning) as Backend, Training Datasets from Kaggle and Apache as local Server.
