Draw a Bayes Net using the five variables. A: ACS C : Lung cancer S : Smoking E : Positive ECG result X : Positive X-ray result - No need to provide the probability distribution for each node in the Bayes Net. - A scanned or photo taken image of your work is fine. Relationships and assumptions: Many patients arriving at an emergency room, suffer from chest pain. This may indicate acute coronary syndrome (ACS). Patients suffering from ACS that go untreated may die with probability 2% in the next few days. Successful diagnosis results lower the short-term mortality rate. Consequently, a prompt diagnosis is essential. Approximately 50% of patients presenting with chest pain turn out to suffer from ACS. About 10% suffer from lung cancer. Of ACS sufferers in general, 2/3 are smokers and 1/3 non-smokers. Only 1/4 of non-ACS sufferers are smokers. In addition, 90% of lung cancer patients are smokers. Only 1/4 of non-cancer patients are smokers. - Assumption 1: A patient may suffer from none, either or both conditions! - Assumption 2: When the smoking history of the patient is known, the development of cancer or ACS are independent. One can perform an ECG to test for ACS. An ECG test has sensitivity of 66.67% (i.e., it correctly detects 2/3 of all patients that suffer from ACS), and a specificity of 75% (i.e., 1/4 of patients that do not have ACS, still test positive). An X-ray can diagnose lung cancer with a sensitivity of 90% and a specificity of 90%. - Assumption 3: Repeated applications of a test produce the same result for the same patient. - Assumption 4: The existence of lung cancer does not affect the probability that the ECG will be positive. Conversely, the existence of ACS does not affect the probability that the X-ray will be positive.