Bayes Network: Lung Cancer Diagnostics Group 7: Hoang-Dung Nguyen, Lily Tran Dr. Vidakovic

Introduction Objective: With given characteristics of an individual’s gender, age, smoking status, carcinogen exposure, CT scan results, and presence of a bloody sputum, a Bayesian Network will be used as a tool to find the probability of an individual being diagnosed with lung cancer. Bayesian Networks Graphical model Probability of random variables and its conditional dependencies Uses Diagnoses of disease, treatment alternatives, treatment outcomes1 Robotics, finance Hoang(Intro and Objective) - In 2013, it is estimated that lung cancer causes more cancer-related deaths than prostate, breast, and colon cancer combined inadequate screening methods to identify patients at earlier stages of the disease. Therefore, we aim to develop a diagnostic screening system using bayesian networks that identify individuals at the highest risk for lung cancer & Lily (Bayesian Network & Uses)

Directed Acyclic Graph (DAG) Independent Variables Predispositions/Causes Condition Hoang - To utilize Bayesian Networks as a diagnostic tool, we developed a Directed Acrylic Graph, which is a causation model Symptom/Effects

Probabilities Legend Age (>60): A Gender (Male): G Smoker: S Carcinogen Exposure: C Lung Cancer: L CT Scan: CT Bloody Sputum: B AC A .816 .1843 GC G .50 CC C Condition .820 .1804 A G .890 .110 A GC .224 .7765 AC G .333 .667 AC GC SC S Condition .916 .0846,7 A G .914 .086 A GC .742 .258 AC G .802 .198 AC GC Lily

Probabilities Legend Age (>60): A Gender (Male): G Smoker: S Carcinogen Exposure: C Lung Cancer: L CT Scan: CT Bloody Sputum: B LC L Condition .910 .0908 C S .931 .069 C Sc .920 .080 Cc S .983 .0179 Cc Sc CTC CT Condition .30 .7010 L .70 LC BC B Condition .43 .5711 L .57 LC Lily

Example 1 Sally is an 80 year old women who smokes and is exposed to carcinogens. She recently got a positive CT scan and has a bloody sputum. What is the probability of her having lung cancer? Hard Evidence: Yes = 1 No = 0 >60 = 1; male = 0; smoker = 1, carcinogen = 1, CT scan = 1, bloody sputum = 1 Probability: ? Lily (state lung cancer symptoms are prominent→ high value )

Example 2 Hard Evidence: Yes = 1 No = 0 John is a 54-year old man who has been experiencing shortness of breath and fatigue. His father has smoked cigarettes throughout his childhood, so he was likely exposed to secondhand smoke for several years. John has a history of smoking but is currently not a smoker. What is the probability that he has lung cancer? Hard Evidence: Yes = 1 No = 0 >60 = 0; male = 1; smoker = 0, carcinogen = 1 Probability: ? Hoang

Example 3 Cara is a 30-year old woman who has led a very healthy lifestyle. She is a nonsmoker who has minimal to no exposure to secondhand smoke. From her recent check up, she had a healthy CT scan and no bloody sputum. What is the probability of Cara being diagnosed with lung cancer? Hard Evidence: Yes = 1 No = 0 >60 = 0; male = 0; smoker = 0, carcinogen = 0, CT scan = 0, bloody sputum = 0 Probability: ? Lily

Conclusion Limitations Values do not take other variables into account (i.e. family history, smoking history) Non-Uniform probabilities based on conditionals in given literature Nonrepresentative Found Probabilities Prevalence of lung cancer is ~0.01% within U.S. 12 Results are estimates of risk, not concrete probabilities Findings Bayes Network has the potential to be a valuable tool for medical diagnostics. Measures a person’s risk for lung cancer, but should not be used alone. Future Improvements Expand our model (DAG) to include all variables that contribute to risk Improving upon the limitations previously stated. Lily (Limitations) → nonuniform example, only depends on age and gender and not region Hoang (Conclusions)

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