{"id":18622173,"url":"https://github.com/simhayn/binary-classification","last_synced_at":"2026-04-20T13:06:48.742Z","repository":{"id":245847019,"uuid":"819370381","full_name":"simhayn/Binary-Classification","owner":"simhayn","description":"Alzheimer's disease detection using XGBoost and other prediction models. ","archived":false,"fork":false,"pushed_at":"2024-08-04T20:58:20.000Z","size":5647,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-05-17T05:36:47.771Z","etag":null,"topics":["alzheimer-disease-prediction","binary-classification","exploratory-data-analysis","mental-health","prediction-model","python","scikit-learn","xgboost"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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/simhayn.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":"2024-06-24T11:19:57.000Z","updated_at":"2024-08-04T20:58:23.000Z","dependencies_parsed_at":"2024-06-27T15:20:50.692Z","dependency_job_id":"4908893e-4e77-4230-aaac-2f5659e2adc8","html_url":"https://github.com/simhayn/Binary-Classification","commit_stats":null,"previous_names":["simhayn/kaggle","simhayn/alzheimer-detection"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/simhayn/Binary-Classification","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/simhayn%2FBinary-Classification","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/simhayn%2FBinary-Classification/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/simhayn%2FBinary-Classification/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/simhayn%2FBinary-Classification/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/simhayn","download_url":"https://codeload.github.com/simhayn/Binary-Classification/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/simhayn%2FBinary-Classification/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32048472,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-20T11:35:06.609Z","status":"ssl_error","status_checked_at":"2026-04-20T11:34:48.899Z","response_time":94,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["alzheimer-disease-prediction","binary-classification","exploratory-data-analysis","mental-health","prediction-model","python","scikit-learn","xgboost"],"created_at":"2024-11-07T04:15:46.307Z","updated_at":"2026-04-20T13:06:48.723Z","avatar_url":"https://github.com/simhayn.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Doctor, is he sick? does he have Alzheimer's disease? 😯🩺 \u003cbr\u003e\nLet's see.. \u003cbr\u003e \nwell, this algorithm can detect if the patient is sick with 96.6% accuracy \u003cbr\u003e\nThe most **relevant features** for this **xgboost** model are as follows:\n1. **Functional Assessment** (ranging from 0 to 10. Lower scores indicate greater impairment)\n2. **ADL (Activities of Daily Living** score, ranging from 0 to 10. Lower scores indicate greater impairment)\n3. **MMSE (Mini-Mental State Examination** score, ranging from 0 to 30. Lower scores indicate cognitive impairment)\n4. **Memory Complaints**\n5. **Behavioral Problems**\n\nAnd there are some more (not so much contributing) features:  \n\n6. Cholesterol HDL\n7. Diet Quality\n8. Sleep Quality\n9. Alcohol Consumption\n10. Cholesterol Triglycerides\n\nthe model was fit to patients who had normal levels of cholesterol and blood pressure, in the ages of 60-90 years old.\n\n## Binary Classification- Alzheimers\nIn this project I explored the 🧠 Alzheimers disease dataset downloaded from Kaggle.com\n\n\nMain steps:\n\n- **Exploratory Data Analysis** (EDA): Visualize features' relationships and distributions. Check some statistics.\n- **Data Preprocessing**: Scale the cumulative features. Covert categorical features to binary.\n- **Model Training**: Train some models on the preprocessed data *(Models: SVM, Logistic Regression, Random Forest, GBoost, Naive Bayes, XGBoost with Random searchCV, Neural Networks).* Select 10 features with higher importance to improve the models. \n- **Model Evaluation**: Evaluate the models' performances. Plot the Neural Networks' validation graph.\n\n\nHope you enjoy this project! \n\nThere are more projects of binary classification for healthcare datasets in this repo. feel free to check them out!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsimhayn%2Fbinary-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsimhayn%2Fbinary-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsimhayn%2Fbinary-classification/lists"}