{"id":19858018,"url":"https://github.com/ajay-308/multiple-disease-predictor","last_synced_at":"2026-04-19T06:36:01.074Z","repository":{"id":210619233,"uuid":"727039149","full_name":"Ajay-308/multiple-disease-predictor","owner":"Ajay-308","description":null,"archived":false,"fork":false,"pushed_at":"2023-12-13T17:33:40.000Z","size":946,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-11T14:22:12.952Z","etag":null,"topics":["api","css3","database","flask","html5","machine-learning","sqlalchemy"],"latest_commit_sha":null,"homepage":"https://disease1-nlf8.onrender.com/","language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Ajay-308.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-12-04T03:49:29.000Z","updated_at":"2025-01-09T19:15:24.000Z","dependencies_parsed_at":"2024-11-12T14:21:21.358Z","dependency_job_id":"82fe8314-a314-468f-b46c-6cb0570ecb9f","html_url":"https://github.com/Ajay-308/multiple-disease-predictor","commit_stats":null,"previous_names":["ajay-308/multiple-disease-predictor"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ajay-308%2Fmultiple-disease-predictor","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ajay-308%2Fmultiple-disease-predictor/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ajay-308%2Fmultiple-disease-predictor/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ajay-308%2Fmultiple-disease-predictor/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Ajay-308","download_url":"https://codeload.github.com/Ajay-308/multiple-disease-predictor/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241255116,"owners_count":19934815,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["api","css3","database","flask","html5","machine-learning","sqlalchemy"],"created_at":"2024-11-12T14:20:49.747Z","updated_at":"2026-04-19T06:35:56.016Z","avatar_url":"https://github.com/Ajay-308.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Project: Multiple-Disease-Predictor-ML-Flask-WebApp\n### Project Intro/Objective \n\nIt's an end-to-end Machine Learning Project. The purpose of this project is to predict whether a person is suffering from a particular disease or not on the basis of his/her input data. The prediction has been done by using Machine Learning (ML) classification algorithms . Currently, this web app can predict 3 types of diseases (Diabetes, Parkinson's and Heart Disease). \n\n\n\n\n### Screenshots \n\n![Screenshot 2023-03-18 at 3 34 48 PM](https://user-images.githubusercontent.com/110475111/226099285-8dc371e0-a9f8-485b-b2ed-c40b8b899b0f.png)\n\n![Screenshot 2023-03-18 at 3 35 01 PM](https://user-images.githubusercontent.com/110475111/226099298-418bf7b0-f102-4d58-a4b9-c29052019582.png)\n\n![Screenshot 2023-03-18 at 3 35 11 PM](https://user-images.githubusercontent.com/110475111/226099303-51995af7-8bd0-4bbb-8776-50551dfb6e55.png)![Screenshot 2023-03-18 at 3 35 19 PM](https://user-images.githubusercontent.com/110475111/226099308-33ad3dbc-90e7-4f8c-812d-b5613b4ad1ea.png)\n\n![Screenshot 2023-03-18 at 3 35 24 PM](https://user-images.githubusercontent.com/110475111/226099309-d0e71a8b-ecb8-4fb7-9e00-b6cc00043169.png)\n\n\n\n![Screenshot 2023-03-18 at 3 35 39 PM](https://user-images.githubusercontent.com/110475111/226099321-4e532f92-420c-47ea-b857-50a7809889f6.png)\n\n![Screenshot 2023-03-18 at 3 36 15 PM](https://user-images.githubusercontent.com/110475111/226099326-7808814b-d2af-45a0-97b0-46f2a15df77c.png)!\n![Screenshot 2023-03-18 at 3 35 33 PM](https://user-images.githubusercontent.com/110475111/226099417-cd525a82-2625-44f6-835f-d1b0e5c8ff29.png)\n\n![Screenshot 2023-03-18 at 3 36 54 PM](https://user-images.githubusercontent.com/110475111/226099346-cf7356bb-c974-489a-b5a6-34faa863c1a3.png)\n![Screenshot 2023-03-18 at 3 37 17 PM](https://user-images.githubusercontent.com/110475111/226099353-1d3daafd-5fda-4661-849c-7e38f9e436fd.png)\n![Screenshot 2023-03-18 at 3 37 49 PM](https://user-images.githubusercontent.com/110475111/226099358-553d7afa-58c5-4220-8b82-e1c5e6614bdf.png)![Screenshot 2023-03-18 at 3 39 25 PM](https://user-images.githubusercontent.com/110475111/226099364-75a5baed-c209-4e46-81bd-5a3e69a8df9f.png)\n\n![Screenshot 2023-03-18 at 3 41 02 PM](https://user-images.githubusercontent.com/110475111/226099373-ab9a1210-e156-4cec-a620-c5b1cfd59a9c.png)\n\nThe datasets that are used for training the ML models are:\n\n- **The diabetes dataset consists of 768 data points, with each datapoint having 8 features. This dataset is Pima Indians Diabetes Database found on the kaggle.**\n\n**Features**\n1. `Pregnancies`: Number of times pregnant\n2. `Glucose`: Plasma glucose concentration a 2 hours in an oral glucose tolerance test\n3. `BloodPressure`: Diastolic blood pressure (mm Hg)\n4. `SkinThickness`: Triceps skin fold thickness (mm)\n5. `Insulin`: 2-Hour serum insulin (mu U/ml)\n6. `BMI`: Body mass index (weight in kg/(height in m)^2)\n7. `DiabetesPedigreeFunction`: Diabetes pedigree function\n8. `Age`: Age (years)\n\n\n**Target Variable**\n9. `Outcome`: Class variable (0 or 1) 268 of 768 are 1, the others are 0\n\n- **The heart dataset consists of 1025 data points, with each datapoint having 13 features. This dataset is Heart Disease Dataset found on the kaggle.**\n\n**Features**\n1. `age`: age in years\n2. `sex`: (1 = male; 0 = female)\n3. `cp`: chest pain type\n4. `trestbps`: resting blood pressure (in mm Hg on admission to the hospital)\n5. `chol`: serum cholestoral in mg/dl\n6. `fbs`: (fasting blood sugar \u003e 120 mg/dl) (1 = true; 0 = false)\n7. `restecg`: resting electrocardiographic results\n8. `thalach`: maximum heart rate achieved\n9. `exang`: exercise induced angina (1 = yes; 0 = no)\n10. `oldpeak`: ST depression induced by exercise relative to rest\n11. `slope`: the slope of the peak exercise ST segment\n12. `ca`: number of major vessels (0-3) colored by flourosopy \n13. `thal`: 0 = normal; 1 = fixed defect; 2 = reversable defect\n\n\n**Target Variable**\n14. `target`: Class variable (0 or 1) 526 of 1025 are 1, the others are 0. Value 0 = no heart disease and 1 = heart disease\n\n- **The ParkinsonsDisease dataset consists of 195 data points, with each datapoint having 22 features. This dataset is Parkinsons Disease Dataset found on the kaggle.**\n\n**Features**\n1. `MDVP:Fo(Hz)`: Average vocal fundamental frequency\n2. `MDVP:Fhi(Hz)`: Maximum vocal fundamental frequency\n3. `MDVP:Flo(Hz)`: Minimum vocal fundamental frequency\n4. `MDVP:Jitter(%)`\n5. `MDVP:Jitter(Abs)`\n6. `MDVP:RAP`\n7. `MDVP:PPQ`\n8. `Jitter:DDP`: Several measures of variation in fundamental frequency\n9. `MDVP:Shimmer`\n10. `MDVP:Shimmer(dB)`\n11. `Shimmer:APQ3`\n12. `Shimmer:APQ5`\n13. `MDVP:APQ`\n14. `Shimmer:DDA` :Several measures of variation in amplitude\n15. `NHR`\n16. `HNR`: Two measures of ratio of noise to tonal components in the voice\n17. `RPDE`\n18. `DFA`: Signal fractal scaling exponent\n19. `spread1`\n20. `spread2`\n21. `PPE`: Three nonlinear measures of fundamental frequency variation\n22. `D2`: Two nonlinear dynamical complexity measures\n\n\n**Target Variable**\n23. `status`: Class variable (0 or 1) 147 of 195 are 1, the others are 0. Value 1 - Parkinson's, 0 - healthy\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fajay-308%2Fmultiple-disease-predictor","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fajay-308%2Fmultiple-disease-predictor","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fajay-308%2Fmultiple-disease-predictor/lists"}