PredictiveModel.java

package com.healthcare;

import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.ml.classification.LogisticRegression;
import org.apache.spark.ml.feature.VectorAssembler;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;

public class PredictiveModel {
    private SparkSession spark;

    public PredictiveModel(SparkSession spark) {
        this.spark = spark;
    }

    public void trainModel(JavaRDD<MedicalRecord> records) {
        // Convert to DataFrame and train the model
        Dataset<Row> trainingData = ... // Convert JavaRDD to DataFrame
        VectorAssembler assembler = new VectorAssembler()
                .setInputCols(new String[]{"feature1", "feature2"}) // Add more features
                .setOutputCol("features");

        Dataset<Row> assembledData = assembler.transform(trainingData);
        LogisticRegression lr = new LogisticRegression().setLabelCol("diagnosis").setFeaturesCol("features");
        lr.fit(assembledData);
    }
}