{"id":24936786,"url":"https://github.com/anarya22/heart-disease-classification","last_synced_at":"2026-05-01T08:32:28.435Z","repository":{"id":273039413,"uuid":"918532861","full_name":"Anarya22/Heart-Disease-Classification","owner":"Anarya22","description":"Predicting heart disease using machine learning. 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Problem Definition\n2. Data\n3. Evaluation\n4. Features\n5. Modelling\n6. Experimentation\n\n## 1. Problem Definition\nIn a statement,\n\u003e Given clinical parameters about a patient, can we predict whether or not they have heart disease?\n\n## 2. Data\nThe original data came from the Cleavland data from the UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/heart+Disease\n\nThere is also a version of it available on Kaggle. https://www.kaggle.com/datasets/sumaiyatasmeem/heart-disease-classification-datas\n\n## 3. Evaluation\n\u003e If we can reach 95% accuracy in predicting whether or not a patient has heart disease during the proof of concept, we'll pursue the project.\n\n## 4. Features\nWhich features of the data will be important to us?\n\nFeatures are different parts and characteristics of the data.\nDuring this step, you'll want to start exploring what each portion of the data relates to and then create a reference you can use to look up later on.\nOne of the most common ways to do this is to create a data dictionary.\nHeart Disease Data Dictionary\n\nA data dictionary describes the data you're dealing with.\nThe following are the features we'll use to predict our target variable (heart disease or no heart disease).\n\nFeature \u003e  Description \u003e    Example Values\nage \u003e\tAge in years \u003e \t29, 45, 60\nsex \u003e\t1 = male; 0 = female \u003e  \t0, 1\ncp\t\u003e    Chest pain type\t\u003e   0: Typical angina (chest pain), 1: Atypical angina (chest pain not related to heart), 2: Non-anginal pain (typically esophageal spasms (non heart related), 3: Asymptomatic (chest pain not showing signs of disease)\ntrestbps \u003e\tResting blood pressure (in mm Hg on admission to the hospital)\t\u003e    120, 140, 150\nchol \u003e\tSerum cholesterol in mg/dl \u003e \t180, 220, 250\nfbs \u003e\t{Fasting blood sugar \u003e 120 mg/dl (1 = true; 0 = false)}\t \u003e   0, 1\nrestecg \u003e \tResting electrocardiographic results \u003e \t0: Nothing to note, 1: ST-T Wave abnormality, 2: Left ventricular hypertrophy\nthalach \u003e \tMaximum heart rate achieved \u003e \t160, 180, 190\nexang \u003e \tExercise induced angina (1 = yes; 0 = no) \u003e   \t0, 1\noldpeak \u003e\tST depression (heart potentially not getting enough oxygen) induced by exercise relative to rest \u003e\t0.5, 1.0, 2.0\nslope \u003e\tThe slope of the peak exercise ST segment \u003e\t0: Upsloping, 1: Flatsloping, 2: Downsloping\nca \u003e \tNumber of major vessels (0-3) colored by fluoroscopy \u003e\t0, 1, 2, 3\nthal \u003e\tThalium stress result \u003e\t1: Normal, 3: Normal, 6: Fixed defect, 7: Reversible defect\ntarget \u003e   \tHave disease or not (1 = yes; 0 = no)\t\u003e     0, 1\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanarya22%2Fheart-disease-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fanarya22%2Fheart-disease-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanarya22%2Fheart-disease-classification/lists"}