DiabetesML AI

Scalable Diabetes Prediction System using PySpark

Demo Link:

Github Repo Url: Link

The Challenge

Early detection of diabetes is crucial but manual screening is time-consuming and may miss subtle patterns. Healthcare providers need an efficient way to assess diabetes risk from patient data.

The Solution

Built a scalable machine learning system using PySpark that accurately predicts diabetes risk based on health indicators. The model helps healthcare providers identify high-risk patients early for preventive intervention.

Tech Mastery Showcase

PySpark

Used for distributed data processing and machine learning pipeline development.

Python

Core programming language for model development and data analysis.

MLlib

PySpark's machine learning library used for building classification models.

Jupyter

Used for interactive development and documentation.

pandas

Utilized for initial data exploration and preparation.

scikit-learn

Used for model evaluation metrics calculation.

Innovative Logic & Implementation

Data Processing Pipeline

Built data processing pipeline to clean, transform and prepare health data for modeling.

1from pyspark.sql import SparkSession
2 from pyspark.ml.feature import VectorAssembler
3 
4 # Initialize Spark
5 spark = SparkSession.builder.appName("DiabetesPredict").getOrCreate()
6 
7 # Load and process data
8 df = spark.read.csv("diabetes_dataset.csv", header=True, inferSchema=True)
9 
10 # Handle missing values
11 df = df.na.fill(df.select([
12    mean(col).alias(col) for col in df.columns
13 ]).first())

Model Development

Implemented classification models using PySpark ML and evaluated performance.

1from pyspark.ml.classification import LogisticRegression
2 from pyspark.ml.evaluation import BinaryClassificationEvaluator
3 
4 # Split data
5 train, test = final_data.randomSplit([0.7, 0.3])
6 
7 # Train model
8 lr = LogisticRegression(labelCol="Outcome")
9 model = lr.fit(train)
10 
11 # Evaluate
12 predictions = model.transform(test)
13 evaluator = BinaryClassificationEvaluator()
14 auc = evaluator.evaluate(predictions)

Overcoming Challenges

Data Quality & Preprocessing

Handling missing values and outliers in health data while maintaining data integrity.

Solution:

Implemented robust data cleaning pipeline with domain-specific validation rules.

Model Selection

Choosing the right model architecture for optimal prediction accuracy.

Solution:

Evaluated multiple classifiers to select best performing model for diabetes prediction.

Scalability

Processing large volumes of patient data efficiently.

Solution:

Leveraged PySpark's distributed computing capabilities for scalable processing.

Key Learnings & Growth

🚀
Gained expertise in building scalable ML pipelines with PySpark.
🚀
Developed skills in healthcare data preprocessing and validation.
🚀
Improved understanding of classification model selection and evaluation.
🚀
Learned best practices for handling sensitive healthcare data.