{'fraction_non_alphanumeric': 0.027976190476190477, 'fraction_numerical': 0.01607142857142857, 'mean_word_length': 5.440613026819923, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Multimedia Based MDSS For Chronic Lung Diseases Diagnosis Using Rule Based Technique

Detecting diseases at early stage can help to overcome and treat them accurately. Identifying the appropriate treatment depends on the method that is used in diagnosing the diseases. Rule Based technique of Artificial Intelligence (AI) is appropriate methodology for medical diagnosis. This paper presents an architecture of Multi Media based Medical Decision Support System (MM-MDSS) for diagnosis of occupational chronic lung diseases. The Expert knowledge was elicited through interview and literature search. A decision tree, along with literature derived probabilities and defined outcome values, were used to model MM-MDSS. The needed diagnostic knowledge was represented using diseases’ profile in the form of rules. Microsoft Visual Basic .Net 2005 and Microsoft SQL Server 2005 were used for the design, implementation and evaluation of the system. 150 samples of 14 occupational chronic lung diseases were used for the system’s implementation, while a Consultant Physician’s interpretation for 24 new cases was used to evaluate the system. Results for Sensitivity, Specificity, Positive Prediction Value and the Negative Prediction Value are 97.7%, 95.0%, 99.2% and 86.3% respectively. Thus, the preliminary results showed promising usage for the MM-MDSS in terms of correct and accurate diagnosis for the inexperienced physician as well as consistent and timely diagnoses for the range of chronic lung diseases that were investigated. KeywordsMulti Media based Medical Decision Support System (MM-MDSS), Expert Knowledge, Occupational Lung Diseases, Decision Tree, Decision Analysis.