This document discusses building a machine learning model to predict heart disease using classification algorithms. It describes collecting patient data on attributes like age, cholesterol levels, and blood pressure to train algorithms like logistic regression, support vector machines, and random forests. Once trained, the model can accurately predict the likelihood of heart disease in new patients, helping healthcare providers make informed decisions to enable early detection and improve outcomes.

1. A MINOR PROJECT ON THE TOPIC
“PREDICTING HEART DISEASE WITH CLASSIFICATION
MACHINE LEARNING ALGORITHMS”
LAKSHMI NARAIN COLLEGE OF TECHNOLOGY & SCIENCE
BHOPAL(M.P.)
DEPARTMENT OF COMPUTER SCIENCE (AIML)
SUBMITTED TO :-
PROF. SHIWALI LATIYAR
ASSISTANT PROFESSOR
SUBMITTED BY :-
DHARITRI RAJ (0157AL211032)
HARSHITA SHARMA (0157AL211047)
B.TECH. II YEAR

2. Heart-y
Predictions: How
Machine Learning
Can Keep Your
Heart in Check

3. Introduction:
- Heart disease is a major health concern
worldwide,and predicting its occurrence
is crucial for early intervention and
prevention.
- Machine learning algorithms offer
promising solutions for accurate
prediction of heart disease based on
patient data.

4. Problem Statement:
- The challenge lies in developing an
accurate classification model that can
reliably identify the presence of heart
disease based on various patient
attributes.
- The goal is to create a robust tool that
can assist healthcare professionals in
making informed decisions and
providing timely interventions.

5. Libraries Used:
- Python libraries such as scikit-learn,
pandas, and numpy are widely utilized
in implementing heart disease
prediction models.
- These libraries provide essential
functions for data preprocessing,
feature selection, model training, and
evaluation.

6. M odel Construction:
-The heart disease prediction model is
built using classification machine
learning algorithms like logistic
regression, support vector machines, or
random forests.
- Relevant patient data,such as age,
cholesterol levels, blood pressure, and
electrocardiogram results, are utilized
as input features.

7. Predictive Capability:
- Once trained on labeled data,the
model can accurately predict the
likelihood of heart disease in new,
unseen patient cases.
- By inputting patient information into
the model,it can analyze the data and
provide a probability or binary
prediction indicating the presence of
heart disease.

8. FutureImplications:
-The heart disease prediction model has significant
implications for healthcare, enabling early detection and
intervention.
-It can aid healthcare providers in making informed
decisions, reducing the risk of complications and
improving patient outcomes.
-Additionally, the model's insights and findings can
contribute to medical research, furthering our
understanding of heart disease risk factors and
prevention strategies.

9. H ear tfelt T h an k s !