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 !