What Do Heart‑Disease Data Reveal? A Python‑Driven Exploratory Analysis

Data Set Loading and Simple Description

First, import the necessary libraries and set up the environment for analysis.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

The dataset contains 303 rows and 14 columns representing various clinical measurements such as age, sex, chest pain type, resting blood pressure, cholesterol, fasting blood sugar, resting ECG, maximum heart rate, exercise‑induced angina, ST depression, slope, number of major vessels, thalassemia and the target label (0 = no disease, 1 = disease).

age: patient age

sex: 1 = male, 0 = female

cp: chest‑pain type (1‑typical angina, 2‑atypical angina, 3‑non‑anginal pain, 4‑asymptomatic)

trestbps: resting blood pressure (mm Hg)

chol: serum cholesterol (mg/dl)

fbs: fasting blood sugar > 120 mg/dl (1 = true, 0 = false)

restecg: resting ECG results (0‑normal, 1‑ST‑T wave abnormality, 2‑left ventricular hypertrophy)

thalach: maximum heart rate achieved

exang: exercise‑induced angina (1 = yes, 0 = no)

oldpeak: ST depression induced by exercise relative to rest

slope: slope of the peak exercise ST segment (1‑upsloping, 2‑flat, 3‑downsloping)

ca: number of major vessels (0‑4) colored by fluoroscopy

thal: thalassemia (3‑normal, 6‑fixed defect, 7‑reversible defect)

target: heart disease (0 = no, 1 = yes)

These variables describe physiological measurements but lack lifestyle factors such as smoking or exercise habits, limiting direct health recommendations.

Gender Ratio and Disease Prevalence

countNoDisease = len(data[data.target == 0])
countHaveDisease = len(data[data.target == 1])
countfemale = len(data[data.sex == 0])
countmale = len(data[data.sex == 1])
print(f'No disease: {countNoDisease}', end=' ,')
print("No disease rate: {:.2f}%".format(countNoDisease / len(data.target) * 100))
print(f'Disease: {countHaveDisease}', end=' ,')
print("Disease rate: {:.2f}%".format(countHaveDisease / len(data.target) * 100))
print(f'Female: {countfemale}', end=' ,')
print("Female ratio: {:.2f}%".format(countfemale / len(data.sex) * 100))
print(f'Male: {countmale}', end=' ,')
print("Male ratio: {:.2f}%".format(countmale / len(data.sex) * 100))

No disease: 138, No disease rate: 45.54% Disease: 165, Disease rate: 54.46% Female: 96, Female ratio: 31.68% Male: 207, Male ratio: 68.32%

Age and Disease Relationship

pd.crosstab(data.age, data.target).plot(kind="bar", figsize=(25,8))
plt.title('Disease Distribution Across Ages')
plt.xlabel('Age')
plt.ylabel('Count')
plt.show()

Age‑Heart Rate‑Disease Relationship

# Scatter plot
plt.scatter(x=data.age[data.target==1], y=data.thalach[data.target==1], c="red")
plt.scatter(x=data.age[data.target==0], y=data.thalach[data.target==0], c="#41D3BD")
plt.legend(["Disease", "No disease"])
plt.xlabel("Age")
plt.ylabel("Maximum Heart Rate")
plt.show()
# Violin plot of resting blood pressure by disease
sns.violinplot(x=data.target, y=data.trestbps, data=data)
plt.show()

Age and Resting Blood Pressure Distribution

plt.scatter(x=data.age[data.target==1], y=data.trestbps[data.target==1], c="#FFA773")
plt.scatter(x=data.age[data.target==0], y=data.trestbps[data.target==0], c="#8DE0FF")
plt.legend(["Disease", "No disease"])
plt.xlabel("Age")
plt.ylabel("Resting Blood Pressure")
plt.show()

The scatter plot shows a fairly uniform distribution of blood pressure across ages for both disease and non‑disease groups, indicating that resting blood pressure alone is not a strong predictor of heart disease in this sample.

Blood Pressure and Heart Rate Relationship

plt.scatter(x=data.thalach[data.target==1], y=data.trestbps[data.target==1], c="#FFA773")
plt.scatter(x=data.thalach[data.target==0], y=data.trestbps[data.target==0], c="#8DE0FF")
plt.legend(["Disease", "No disease"])
plt.xlabel("Maximum Heart Rate")
plt.ylabel("Resting Blood Pressure")
plt.show()

In this dataset, blood pressure and heart rate appear uncorrelated for both disease and non‑disease groups.

Chest Pain Type, Disease and Blood Pressure

sns.swarmplot(x='target', y='trestbps', hue='cp', data=data, size=6)
plt.xlabel('Disease')
plt.show()
fig,ax=plt.subplots(1,2,figsize=(14,5))
sns.countplot(x='cp', data=data, hue='target', palette='Set3', ax=ax[0])
ax[0].set_xlabel('Chest Pain Type')
data.cp.value_counts().plot.pie(ax=ax[1], autopct='%1.1f%%', explode=[0.01,0.01,0.01,0.01], shadow=True, cmap='Blues')
ax[1].set_title('Chest Pain Type Distribution')
plt.show()

Patients with chest‑pain type 0 dominate the non‑disease group, while types 1‑3 are more common among those with disease.

Exercise‑Induced Angina, Disease and Heart Rate

sns.swarmplot(x='exang', y='thalach', hue='target', data=data, size=6)
plt.xlabel('Exercise‑induced Angina')
plt.ylabel('Maximum Heart Rate')
plt.show()

Patients without exercise‑induced angina tend to have higher maximum heart rates (160‑180 bpm) and a higher proportion of disease, whereas those with angina show lower heart rates (120‑150 bpm) and many are disease‑free.

Number of Major Vessels (ca), Blood Pressure and Disease

plt.figure(figsize=(15,5))
sns.swarmplot(y='trestbps', x='ca', hue='target', data=data, palette='RdBu_r', size=7)
plt.xlabel('Number of Major Vessels')
plt.ylabel('Resting Blood Pressure')
plt.show()
plt.figure(figsize=(15,5))
sns.catplot(x='ca', y='age', hue='target', kind='swarm', data=data, palette='RdBu_r')
plt.xlabel('Number of Major Vessels')
plt.ylabel('Age')
plt.show()

Zero major vessels (ca = 0) are strongly associated with disease presence.

Age and Cholesterol Relationship

plt.scatter(x=data.age[data.target==1], y=data.chol[data.target==1], c="orange")
plt.scatter(x=data.age[data.target==0], y=data.chol[data.target==0], c="green")
plt.legend(["Disease", "No disease"])
plt.xlabel("Age")
plt.ylabel("Cholesterol")
plt.show()
sns.boxplot(x=data.target, y=data.chol, data=data)
plt.show()

Cholesterol levels show no clear separation between disease and non‑disease groups; the box plot indicates only a slight decrease in median cholesterol for diseased patients.

Correlation Analysis

plt.figure(figsize=(15,10))
ax = sns.heatmap(data.corr(), cmap=plt.cm.RdYlBu_r, annot=True, fmt='.2f')
a,b = ax.get_ylim()
ax.set_ylim(a+0.5, b-0.5)
plt.show()

The heatmap reveals that the target variable is positively correlated with chest‑pain type (cp), maximum heart rate (thalach) and slope, and negatively correlated with exercise‑induced angina (exang), ST depression (oldpeak), number of vessels (ca) and thalassemia (thal).

Overall, this exploratory analysis visualizes several relationships within the heart‑disease dataset, highlighting gender imbalance, age‑related disease trends, and the limited predictive power of individual clinical measurements without further modeling.