akrishna5/diagnosis-of-heat-disease

Predicting whether a person is suffering from heart disease or not.

Diagnosis of Heart Disease

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We have used a variety of Machine Learning algorithms, implemented in Python, to predict the presence of heart disease in a patient

This is a classification problem, with input features as a variety of parameters, and the target variable as a binary variable, predicting whether heart disease is present or not

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Dataset = heart.csv

Attribute Information

It is composed of 14 attributes which are age, sex, chest pain type, resting blood pressure, serum cholesterol, fasting blood sugar, resting electrocardiographic results, maximum heart rate achieved, exercise induced angina, oldpeak — ST depression induced by exercise relative to rest, the slope of the peak exercise ST segment, number of major vessels and Thalassemia (blood disorder)

Dataset columns:

age: The person’s age in years

sex: The person’s sex (1 = male, 0 = female)

cp: chest pain types

— Value 0: asymptomatic
— Value 1: atypical angina
— Value 2: non-anginal pain
— Value 3: typical angina

trestbps: The person’s resting blood pressure (mm Hg on admission to the hospital)

chol: The person’s cholesterol measurement in mg/dl

fbs: The person’s fasting blood sugar (> 120 mg/dl, 1 = true; 0 = false)

restecg: resting electro-cardiographic results

— Value 0: showing probable or definite left ventricular hypertrophy by Estes’ criteria
— Value 1: normal
— Value 2: having ST-T wave abnormality (T wave inversions or ST elevation or depression of > 0.05 mV)

thalach: The person’s maximum heart rate achieved

exang: Exercise induced angina (1 = yes; 0 = no)

oldpeak: ST depression induced by exercise relative to rest

slope: the slope of the peak exercise ST segment

- Value 0: downsloping
- Value 1: flat
- Value 2: upsloping

ca: The number of major vessels (0–3)

thal: A blood disorder called thalassemia

- Value 0: NULL (dropped from the dataset previously)
- Value 1: fixed defect (no blood flow in some part of the heart)
- Value 2: normal blood flow
- Value 3: reversible defect (a blood flow is observed but it is not normal)

target: Heart disease (1 = NO , 0 = YES)

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Machine Learning algorithms used

1. Logistic Regression
2. Support Vector Machine (SVM)
3. Decision Tree Classification
4. Random Forest Classification

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In Future

Ensemble models like

1. XGBoost Classification
2. AdaBoost
3. Gradient Boosting

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