diff --git a/tests/test_dsm.py b/tests/test_dsm.py
index fe3fe43..891cba8 100644
--- a/tests/test_dsm.py
+++ b/tests/test_dsm.py
@@ -1,3 +1,7 @@
+"""This module contains test functions to
+test the accuracy of Deep Survival Machines
+models on certain standard datasets.
+"""
 import unittest
 
 from dsm import DeepSurvivalMachines
@@ -7,21 +11,74 @@
 import numpy as np
 
 class TestDSM(unittest.TestCase):
+    """Base Class for all test functions"""
+    def test_support_dataset(self):
+        """Test function to load and test the SUPPORT dataset.
+        """
 
-  def test_dsm(self):
+        x, t, e = datasets.load_dataset('SUPPORT')
+        t_median = np.median(t[e==1])
 
-    x, t, e = datasets.load_dataset('SUPPORT')
+        self.assertIsInstance(x, np.ndarray)
+        self.assertIsInstance(t, np.ndarray)
+        self.assertIsInstance(e, np.ndarray)
 
-    self.assertIsInstance(x, np.ndarray)
-    self.assertIsInstance(t, np.ndarray)
-    self.assertIsInstance(e, np.ndarray)
+        self.assertEqual(x.shape, (9105, 44))
+        self.assertEqual(t.shape, (9105,))
+        self.assertEqual(e.shape, (9105,))
 
-    self.assertEqual(x.shape, (9105, 44))
-    self.assertEqual(t.shape, (9105,))
-    self.assertEqual(e.shape, (9105,))
+        model = DeepSurvivalMachines()
+        self.assertIsInstance(model, DeepSurvivalMachines)
+        model.fit(x, t, e, iters=10)
+        self.assertIsInstance(model.torch_model,
+                          DeepSurvivalMachinesTorch)
+        risk_score = model.predict_risk(x, t_median)
+        survival_probability = model.predict_survival(x, t_median)
+        np.testing.assert_equal((risk_score+survival_probability).all(), 1.0)
+
+    def test_pbc_dataset(self):
+        """Test function to load and test the PBC dataset.
+        """
+
+        x, t, e = datasets.load_dataset('PBC')
+        t_median = np.median(t[e==1])
+
+        self.assertIsInstance(x, np.ndarray)
+        self.assertIsInstance(t, np.ndarray)
+        self.assertIsInstance(e, np.ndarray)
+
+        self.assertEqual(x.shape, (1945, 25))
+        self.assertEqual(t.shape, (1945,))
+        self.assertEqual(e.shape, (1945,))
+
+        model = DeepSurvivalMachines()
+        self.assertIsInstance(model, DeepSurvivalMachines)
+        model.fit(x, t, e, iters=10)
+        self.assertIsInstance(model.torch_model,
+                          DeepSurvivalMachinesTorch)
+        risk_score = model.predict_risk(x, t_median)
+        survival_probability = model.predict_survival(x, t_median)
+        np.testing.assert_equal((risk_score+survival_probability).all(), 1.0)
+
+    def test_framingham_dataset(self):
+        """Test function to load and test the Framingham dataset.
+        """
+        x, t, e = datasets.load_dataset('FRAMINGHAM')
+        t_median = np.median(t)
+
+        self.assertIsInstance(x, np.ndarray)
+        self.assertIsInstance(t, np.ndarray)
+        self.assertIsInstance(e, np.ndarray)
+
+        self.assertEqual(x.shape, (11627, 18))
+        self.assertEqual(t.shape, (11627,))
+        self.assertEqual(e.shape, (11627,))
 
-    model = DeepSurvivalMachines()
-    self.assertIsInstance(model, DeepSurvivalMachines)
-    model.fit(x, t, e, iters=10)
-    self.assertIsInstance(model.torch_model,
+        model = DeepSurvivalMachines()
+        self.assertIsInstance(model, DeepSurvivalMachines)
+        model.fit(x, t, e, iters=10)
+        self.assertIsInstance(model.torch_model,
                           DeepSurvivalMachinesTorch)
+        risk_score = model.predict_risk(x, t_median)
+        survival_probability = model.predict_survival(x, t_median)
+        np.testing.assert_equal((risk_score+survival_probability).all(), 1.0)