Prediction of neurological diseases using data mining

Background: and Aim: Data mining is one of the stages of acquiring knowledge in a database to collect useful information. Data mining is a new field that has various applications and it is known as one of the top ten sciences affecting technology. Data mining analyzes databases and massive data sets in order to discover and extract knowledge, and machine (and semi-machine) mines. Such studies and explorations can actually be considered the extension and continuation of the ancient and ubiquitous knowledge of statistics. The major difference is the scale, breadth and variety of fields and applications, as well as the dimensions and sizes of today's data, which require machine learning, modeling, and training methods. In the 1960s, statisticians used the term "Data Fishing" or "Data Dredging" to discover any relationship in a very large volume of data without considering any assumptions. After thirty years and with the accumulation of data in databases, the term "Data Mining" became more popular around 1990. The purpose of this research is to predict brain and nerve diseases using data mining algorithms. The purpose of this research is to help medical professionals to predict disease. Materials: and Methods: In this research, after data preparation, disease prediction has been attempted using large matrix methods and data mining techniques. By examining the new vector, we can find out which of the diseases in the matrix will be closer to this new disease with new symptoms using the rows of the matrix. The conducted research is one of descriptive-analytical and applied studies. Results: : In this research, we used different meters such as Jacquard distance, cosine similarity L 1 -norm and cosine similarity L 2 -norm implemented a program using Python software to predict brain and neurological diseases. Conclusion: The algorithm implemented by Python software, the doctor enters the symptoms of the patient and the output of the program shows three diseases close to the input symptoms for each meter, and finally all the meters are compared and the meter that has a weaker result is determined each time it is run. The advantages of each of these meters are explained below.