Designing an intelligent system for predicting chromosomal genetic diseases using data mining

Background and Aim: Today we are witnessing tremendous advances in medical data mining. The data, by analyzing and discovering the relationships between them, can lead to algorithms that help us prevent or treat many diseases. Meanwhile, genetic diseases have attracted a large part of the attention of the medical world because the birth of children with genetic disorders imposes a great financial, psychological and emotional burden on society. Therefore, the aim of this study is to present an algorithm as a secondary screening test before performing cell and molecular tests.   Material and Methods: In this study, 1000 cases of pregnant women who were in moderate or high risk group after screening tests were studied. Their clinical information was stored, missing data was deleted, and records were integrated. Then, using Clementine software, data mining and data correlation were performed, and finally a suitable algorithm for diagnosing the disease was performed. Genetic mutations were identified.   Results: By applying five algorithms, neural networks, support vector machine, binary decision tree, multiple decision tree and logistic regression on the data, it was found that the neural network algorithm with 97.522% accuracy has the highest success rate in Diagnosis of genetic-chromosomal diseases before birth.   Conclusion: The use of genetic algorithm as a screening test causes less people to be candidates for costly and dangerous cellular and molecular tests and can be used as a tool to help detect the disease. To be used in the medical world.