We are predicting Heart Disease by Taking 14 Medical Parameters as an inputs through 2 data Minning Techniques(Decision Tree(Faster) And KNN neighbour Algorithms(Slower)).
And Visualizing The dataset.If the output 1 then it means Higher Chances of getting Heart Attack ,if 0 then it means Less chances of Heart Attack.
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Heart Attack Prediction using Machine Learning
1. HEART ATTACK PREDICTION BY MACHINE LEARNING(PYTHON)
BY
▪ MOHD
SABER
(17-5038)
▪ MOHD
IHTAESHAM
UDDIN
(17-5045)
(DCET-ECE)
2. Heart Attack Prediction Using Machine Learning
Abstract
Cardiovascular disease is one of the mostheinous disease, especially the silent heart attack, which
attacks a person so abruptly that there’s no time to get it treated and such disease is very difficult to
be diagnosed. Various medical data mining and machine learning techniques are being implemented
to extract the valuable information regarding the heart disease prediction. Yet, the accuracy of the
desired results are not satisfactory. This Model proposes a heart attack prediction system using
Machine learning techniques. Health care field has a vast amount of data, for processing those data
certain techniques are used. Datamining is one of the techniques often used. Heart disease is the
Leading cause of death worldwide. This System predicts the arising possibilities of Heart Disease. The
datasets used are classified in terms of medical parameters. This system evaluates those parameters
using data mining classification technique. The datasets are processed in python programming using
two main Machine Learning Algorithm namely Decision Tree Algorithm and Naive Bayes Algorithm
which shows the best algorithm among these two in terms of accuracy level of heart disease.
• IDLE we used in this project is JUPYTER NOTEBOOK.
• Requirements: Laptop with min 4 GB RAM, Anaconda,Jupyter Notebook, Python 3.7
3. What is Machine Learning?
programming a computer, to optimize performance standards, by using past experience
Machine learning is abranch of Artificial Intelligence
Calculation of algorithms allow computers to develop behavior's based on real data
4. Quick facts about Machine Learning
Machine learning
algorithms
Supervised
algorithms
Apply pastinformation
registered,tonewdata
Unsupervised
algorithms
Draw conclusions
from datasets
Reinforcement
algorithms
To make a sequence of
decisions.
6. Case studies on Machine Learning
If amember
frequently “likes”
a friend’sposts,
thenews feedwill
automatically
start showing
more ofthat
friend’sactivity,
earlier inthe feed.
Machinelearning
algorithms have
helped reveal
previously
unrecognized
influences
between artists.
Netflixpredicts
the ratings an
individual will
give a movie,
which they
haven’teven
watchedyet,
based onprevious
movie ratings
made bythem.
7. Statistical method used to recommend a
movie on Netflix
Anybody can
ask aquestion
Anybodycan
answer
The bestanswers
are votedupand
rise to thetop
8. Industries which will benefit because of
Machine Learning and Artificial Intelligence
•AI financialadvisors willsoon replace
human advisors,ascomputerizedsystems
can scan tensof thousands of enterprises
to makequickrecommendations.
Finance
•Sequencing of individual genomes and
comparing them to a large database, will
allow doctors andAI bots to predict the
probability of contracting a particular
disease and a remedy to treatit, when it
appears.
Healthcare
11. GET DATA:
Kaggle which is so organized. They give you info on the features, data types, number of
records.
You can use their kernel too and you won’t have to download the dataset.
Reddit which is great for requesting the datasets we want.
Google Dataset Search which is still Beta, but it’s amazing.
UCI Machine Learning Repository, this one maintains 468 data sets as a service to the
machine learning community.
14. PROPOSED ALGORITHM:
Why use DecisionTrees?
There are various algorithms in Machinelearning, so choosing the best algorithm for the given dataset
and problem is the main point to remember while creating a machine learning model. Below are the two
reasons for using the Decision tree:
Decision Trees usuallymimic human thinkingabilitywhile making a decision, so it is easy to understand.
The logic behind the decision tree can be easily understood because it shows a tree-like structure.
EXAMPLE:
15. KN NEIGHBOUR CLASSIFIER
The k-nearest neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning
algorithm that can be used to solve both classification and regression problems
A classificationproblem has a discrete value as its output. For example, “likespineappleon pizza” .
A regression problem has a real number (a number with a decimal point) as its output.
The KNN Algorithm
1.Load the data
2.InitializeK to your chosen number of neighbors
3. For each example in the data
3.1 Calculatethe distance between the query example and the current example from the data.
3.2 Add the distance and the index of the example to an ordered collection
4. Sort the ordered collection of distances and indices from smallest to largest (in ascending order) by
the distances
5. Pick the first K entries from the sorted collection
6. Get the labels of the selected K entries
7. If regression, return the mean of the K labels
8. If classification, return the mode of the K labels
18. ABOUT THE DATA SET:
Age : Age of the patient
Sex : Sex of the patient
ca: number of major vessels (0-3)
cp : Chest Pain type
Value 1: typical angina
Value 2: atypicalangina
Value 3: non-anginal pain
Value 4: asymptomatic
trtbps : resting blood pressure (in mm Hg)
chol : cholestoral in mg/dl fetched via BMI sensor
fbs : (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)
restecg : resting electrocardiographicresults
Value 0: normal
Value 1: having ST-T wave abnormality(T wave inversions and/orST elevationor depression of >
0.05 mV)
Value 2: showing probableor definite left ventricularhypertrophy by Estes' criteria
thalach: maximum heart rate achieved
target : 0= less chance of heart attack 1= more chance of heart attack
