By means of data mining techniques, we can exploit furtive and precious information through medicine data bases. Because of huge amount of this information, study and analyses are too difficult. We want some methods to exploring through data and extract valuable information which can be used in the future similar cases. One of these cases is accouchement. The mechanism of accouchement is a natural and spontaneous process without the need to any intervention. In some conditions, maybe mother, baby or both of them are in hazard and need help and support. This help is provided by Caesarian Section which saves mother and baby. Nevertheless, we need to know when we should use surgery. This study explains utilization of medical data mining in determination of medical operation methods. We render this with accumulating 80 pregnant women information. The results show that decision tree algorithm designed for this case study generates correct prediction for more than 86.25% tests cases

Artificial Neural Network (ANN)-based diagnosis of medical diseases has been taken into great consideration in recent years. In this paper, two types of ANNs are used to classify effective diagnosis of Parkinson's disease. Multi-Layer Perceptron (MLP) with back-propagation learning algorithm and Radial Basis Function (RBF) ANNs were used to differentiate between clinical variables of samples (N = 195) who were suffering from Parkinson's disease and who were not. For this purpose, Parkinson's disease data set, taken from UCI machine learning database was used. Mean squared normalized error function was used to measure the usefulness of our networks during trainings and direct performance calculations. It was observed that MLP is the best classification with 93.22% accuracy for the data set. Also, we got 86.44% accuracy in RBF classification for the same data set. This technique can assist neurologists to make their ultimate decisions without hesitation and more astutely.

In recent years, applications of data mining methods are become more popular in many fields of medical diagnosis and evaluations. The data mining methods are appropriate tools for discovering and extracting of available knowledge in medical databases. In this study, we divided 11 data mining algorithms into five groups which are applied to a dataset of patient's clinical variables data with Parkinson's Disease (PD) to study the disease progression. The dataset includes 22 properties of 42 people that all of our algorithms are applied to this dataset. The Decision Table with 0.9985 correlation coefficients has the best accuracy and Decision Stump with 0.7919 correlation coefficients has the lowest accuracy.