Add the dataset for the cardiovascular disease detection

examples/healthcare/data/cardiovascular.py

@@ -0,0 +1,63 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+
+import numpy as np
+import os
+import sys
+
+def load_cardiovascular_data(file_path):
+    data = np.loadtxt(file_path, delimiter=',')  
+
+
+    X = data[:, :-1]  
+    y = data[:, -1]   
+
+
+    # Split the data into training and validation sets
+    train_size = int(0.8 * data.shape[0])
+    train_x, val_x = X[:train_size], X[train_size:]
+    train_y, val_y = y[:train_size], y[train_size:]
+
+    # Normalize the data
+    mean = np.mean(train_x, axis=0)
+    std = np.std(train_x, axis=0)
+    train_x = (train_x - mean) / std
+    val_x = (val_x - mean) / std
+
+    return train_x, train_y, val_x, val_y
+
+def load():
+    file_path = 'cardio_train.csv'  #need to change
+
+    train_x, train_y, val_x, val_y = load_cardiovascular_data(file_path)
+
+    train_x = np.array(train_x, dtype=np.float32)
+    val_x = np.array(val_x, dtype=np.float32)
+    train_y = np.array(train_y, dtype=np.int32)
+    val_y = np.array(val_y, dtype=np.int32)
+
+    return train_x, train_y, val_x, val_y
+
+if __name__ == '__main__':
+    train_x, train_y, val_x, val_y = load()
+    print("Training data shape:", train_x.shape)
+    print("Training labels shape:", train_y.shape)
+    print("Validation data shape:", val_x.shape)
+    print("Validation labels shape:", val_y.shape) 
+