Neurological Disease Prediction Based on EEG Signals Using Machine Learning Approaches

Diagnostics and prognoses of brain disorders can be greatly aided by machine learning. To bring these tools into clinical routine, we argue that key challenges remain to be addressed by the community. To overcome the limitations of black-box approaches, we need to use interpretable models to overcome the shortcomings of validation and reproducible research practices. Extensive generalization studies are required. Many people die each year from brain diseases, which are the most prevalent. As the death toll continues to rise, it is estimated that it will reach 75 million by 2030. We cannot predict brain diseases with modern technology or an advanced healthcare system. In our paper, we use machine learning algorithms to implement the neurological disease prediction approach since such algorithms are a critical source of data prediction. MIT-BIH repository data comprising a variety of patients were used as the database. Based on the classifiers utilized, the findings have proven that the RDF produced the most accurate result with 96.32% accuracy.