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Advancing Healthcare through Python: Predicting Heart Disease at an Early Stage
Read more to know how our client from the healthcare industry harnessed our expertise in Python technology to build a robust predictive model for heart disease detection.
Every organization is on the verge of using Machine Learning to analyze the data they generate daily from the users. Actually, data-driven decision-making plays a crucial role in driving innovation and success. This helps them to predict the testing data in the production environment.
Therefore, one of our clients from the healthcare industry recognized the significance of harnessing the power of data analysis to uncover insights that can lead to better products, improved patient outcomes, and efficient operations.
Understanding the Real Time Scenario
Heart disease is a significant global health issue, responsible for a huge number of deaths every year. Therefore, it’s essential to detect and predict heart disease at an early stage for effective treatment and improved patient outcomes.
So, by keeping this challenge in mind, our client – a prominent healthcare provider from the USA, initiated a project focused on leveraging the power of Python technology to develop a robust predictive model for heart disease detection.
In this tech study, we will explore one of the datasets to build a machine-learning model. This model enabled our client to anticipate the probability of a patient with heart disease accurately. As an experienced Python development company, we will also discuss how our experienced Python developers implemented AutoML with Python.
Well, the importance of Python in such a real-time scenario holds immense potential for implementation in healthcare industry, facilitating timely interventions by medical professionals to ensure prompt treatment.
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Here’s the AutoML pipeline model we followed for our client.
Challenges Our Python Developers Found in Heart Disease Prediction
Before integrating Python technology into our project, our Python developers found several key challenges in the prediction of heart disease:
Complex Data Patterns
Our US-based client – a renowned healthcare provider, dealt with a huge amount of data from various sources, including medical records, patient history, diagnostic tests, and lifestyle factors. Extracting meaningful patterns from this complex dataset was a tough task that required advanced analytical techniques.
Accuracy and Reliability
Existing prediction methods lacked the desired accuracy and reliability. The consequences of false positives and false negatives in heart disease prediction were significant, leading to incorrect diagnoses and unnecessary patient anxiety.
Scalability
As the patient database expanded over time, the computational demands of data analysis and model training grew. Ensuring that the predictive model could scale to accommodate a larger dataset was a concern.
Implementation of Python for Heart Disease Prediction
Here are some steps we followed to implement Python for our client.
Step 1: The first step we followed was to import some essential libraries like:
- NumPy for Matrix Manipulation
- Pandas for Data Analysis
- Matplotlib for Data Visualization
Python's visualization libraries played a crucial role in conducting exploratory data analysis. These tools enabled our team to visualize relationships between variables, identify trends, and gain insights into the underlying data patterns.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import h2o
from h2o.automl import H2OAutoML
Step 2: Then we utilized the Pandas data frame to store data optimized. However, that helped us to load our dataset as well. We performed Data preprocessing to prepare data for further modeling and generalization using the dataset.
# Initialize H2O
h2o.init()
# Load and convert the dataset to H2OFrame
hf = h2o.import_file("heart_disease.csv")
Step 3: Once the data for the machine learning model was finalized, we used the popular automated machine learning library – H2O.ai, which helped us create and train the model.
The platform’s prime objective is to provide high-level APIs which enable seamless automation of many pipeline stages, including feature engineering, model selection, data cleaning, hyperparameter tuning, and many more.
This amazing capability substantially reduced the time required to train machine learning models for any data science project.
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Leverage Python's Potential
Step 4: We utilized the H2O library to build the model. However, we encountered a challenge where we had to specify the type of problem – a regression problem, classification problem, or other type with the target variable mentioned.
After specifying the problem, the library chose the best model as per requirements, including algorithms such as Decision Trees, Deep Neural Networks, Support Vector Machines, etc.
# Split the data into training, testing, and validation sets
train, test, validation = hf.split_frame(ratios=[0.7, 0.15])
# Define the target variable and problem type
target_variable = "target"
problem_type = "binary"
Step 5: Now this step was the most challenging task for our software developers to fine-tune the model based on the hyperparameters involved. The tuning process consisted of various techniques like Grid-search Cross Validation.
This iterative process ensured that the predictive model achieved the highest possible accuracy. We used cross-validation techniques to validate the model's generalization capacity.
# Initialize and run AutoML
automl = H2OAutoML(max_models=10, seed=1, balance_classes=True)
automl.train(y=target_variable, training_frame=train, validation_frame=validation)
# Display the leaderboard
leaderboard = automl.leaderboard
print(leaderboard)
# Retrieve the best model
best_model = automl.leader
Step 6: The last step was to assess the model's performance in classification and regression problems using evaluation metrics like the confusion matrix, recall, precision, and others to conclude how well our model would perform in a real-world setting.
# Generate predictions on the test data
predictions = best_model.predict(test)
# Convert predictions to a Pandas DataFrame
predictions_df = predictions.as_data_frame()
# Evaluate the model's performance using accuracy, precision, recall, and F1-score
evaluation = best_model.model_performance(test)
accuracy = evaluation.accuracy()[0]
precision = evaluation.precision()[0]
recall = evaluation.recall()[0]
f1_score = evaluation.F1()[0]
# Display evaluation metrics
print("Accuracy:", accuracy)
print("Precision:", precision)
print("Recall:", recall)
print("F1-score:", f1_score)
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Step 7: Finally, we marked the ROC curve, which displayed the graph between false positive rate and false negative rate, as well as printed the confusion matrix and event data. False positive rate means that our model is predicting the wrong result compared to the actual and predicts the positive class when it belongs to the negative class. Then we turned off the H2O.
# Visualize the ROC curve
roc_curve = best_model.roc()
roc_curve.plot()
plt.title("Receiver Operating Characteristic (ROC) Curve")
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.show()
# Display the confusion matrix
confusion_matrix = best_model.confusion_matrix()
confusion_matrix.plot()
plt.title("Confusion Matrix")
plt.xlabel("Predicted")
plt.ylabel("Actual")
plt.show()
# Terminate the H2O session
h2o.cluster().shutdown()
The Final Outcome
The implementation of Python technology yielded substantial results and had a positive impact on heart disease prediction for our client:
High Accuracy: The predictive model developed using Python achieved a high accuracy rate in predicting heart disease. This accuracy reduced the occurrence of false positives and false negatives, leading to more reliable diagnoses and improved patient care.
User-Friendly Interface: As an experienced Python app development company, we utilized Python’s web framework - Django to streamline the prediction process. Also, the intuitive interface allowed for easy input of patient data to predictive results.
Cost-Efficiency: The accurate identification of individuals at risk of heart disease resulted in reduced unnecessary diagnostic tests and treatments. This cost-efficient approach optimized resource allocation within their healthcare system.
Our Expertise in Python to Revolutionize the Healthcare App
Our client’s initiative to predict heart disease using Python technology highlights the transformative potential of data-driven approaches in healthcare. We created a robust predictive model by utilizing extensive Python frameworks and libraries for data manipulation, visualization, machine learning, and web development.
As the healthcare industry evolves, our contributions as the #1 provider of healthcare app development services to data-driven solutions using Python remain a powerful force in enhancing patient well-being and healthcare efficiency.
