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In this article, we are talking about Heart Attack prediction models. Here we will give you complete information about the Heart Attack prediction model.
Heart failure disease affects more people in the world than autoimmune conditions.Cardiovascular Diseases affect the heart more and obstruct blood flow through the blood vessels. Chronic ailments include heart attack, strokes, congestive heart failure etc.
Project Idea
The aim of this project is to detect whether patients have heart disease or not by giving a number of features from patients. The causes of heart disease are diabetes, obesity, unhealthy diet, being overweight, excessive alcohol consumption, and being inactive in physical form etc. biological factors age, thalassemia, chest pain, blood pressure, thal rate etc. This machine learning project we use these biological factors to predict the heart attack prediction model.
Dataset
To build the Heart attack prediction model we have used a heart dataset. The data file heart.csv contains the information used to create the model. It consists of 303 rows and 14 columns. The columns represent the variables, while the rows represent the instances.
The Dataset is composed of four concepts.
Data source
Variables
Instances
Missing values
This dataset uses the following 9 variables:
Age : Age of person
sex: Gender of the person
Cp : chest pain type
Trtbps : resting blood pressure (in mm Hg)
Chol : cholesterol in mg/dl fetched via BMI sensor
Fbs : (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)
restecg: resting electrocardiographic results
Thalachh : maximum heart rate achieved
Exng : exercise induced angina (1 = yes; 0 = no)
slp : slop
cpp : number of major vessels (0-3)
thall : Thal rate
output : Target variable
In our explanation video of Data-driven Heart Attack prediction model using python, We cover techniques of exploratory analytics, data aggregation and cleansing, feature engineering, more importantly, model building and evaluation. We utilised Random Forest Classifier, Support Vector Machine, K-Nearest Neighbour and Logistic Regression with best parameters possible for getting the best prediction accuracy. All these algorithms are mathematical implementations and we have utilised them with optimal parameters.
The Heart Attack Prediction Project is described in two videos part 1 and part 2.
Part 1 : Title : HEART ATTACK PREDICTION Project Part 1 | AI ML Project Series
Description : This is the introduction part of HEART ATTACK PREDICTION Project where we provide the details and procedures of the coming project that we will build in Part2 of this Series. This is based on analysis of a patient’s diagnosis conditions like blood pressure, random sugar, cholesterol, resting pressure, pulse etc. to analyse whether or not the patient is probable for a heart attack risk. The result will be that we will be able to analyse on the basis of a patient's test results whether they have a risk of heart attack or not.
Part 2 : Title : HEART ATTACK PREDICTION Project Part 2 | AI ML Project Series
Description : This is the second part of the HEART ATTACK PREDICTION Project where we create a complete project on Kaggle Community Platform regarding prediction of Heart attack risk of a patient based on their various test results. We use data cleaning, data plotting and utilised Random Forest Classifier, Support Vector Machine , K Nearest Neighbours and Logistic Regression with best parameters possible for getting the best prediction accuracy. All these algorithms are mathematical implementations and we have utilised them with optimal parameters.
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