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Data mining techniques are increasingly used in clinical decision making and help the physicians to make more accurate and effective decisions. In this chapter, a classification of data mining applications in clinical decision making is presented through a systematic review. The applications of data mining techniques in clinical decision making are divided into two main categories: diagnosis and treatment. Early prediction of medical conditions, detecting multi-morbidity and complications of diseases, identifying and predicting the chronic diseases and medical imaging are the subcategories which are defined in the diagnosis part. The Treatment category is composed of treatment effectiveness and predicting the average length of stay in hospital. The majority of the reviewed articles are related to diagnosis and there is only one article which discusses the determination of drug dosage in successful treatment. The classification model is the most commonly practical model in the clinical decision making.

Root-mean-square-deviation (RMSD) is an indicator in protein-structure-prediction-algorithms (PSPAs). Goal of PSP algorithms is to obtain 0 Å RMSD from native protein structures. Protein structure and RMSD prediction is very essential. In 2013, the estimated RMSD proteins based on nine features were obtained best results using D2N (Distance to the native). We presented in This paper proposed approach to reduce predicted RMSD Error Than the actual amount for RMSD and calculate mean absolute error (MAE), through feed forward neural network, adaptive neuro fuzzy method. ANFIS is achieved better and more accurate results.

According to the World Health Organization, Tb is the biggest cause of death among the infectious diseases. Due to the high percentage of people with tuberculosis infection and the high number of death among these patients, this study is a prospective study aimed to categorize and find the relationship between different clinical and demographic characteristics. The study was conducted on 600 patients from Masih-e-Daneshvari tuberculosis research center during 2015-2016. The K-Means clustering data mining algorithms and decision trees are used to perform the categorization and determine common indicators among patients. 2 clusters according to Dunn index were chosen as the optimal clusters. Common factors between clusters are provided in detail in the findings section. According to the results of this study, the most important factors identified by the clustering include hemoglobin, age, sex, smoking, alcohol consumption and creatinine. The RBF neural network tree has 98% accuracy. According to the results of this study, the most important factors identified are sex, smoking, alcohol consumption and WBC, albumin.