Design of an Efficient Model for Psychological Disease Analysis and Prediction Using Machine Learning and Genomic Data Samples

Abstract

There is a rapid growth in mental disorders, thus leading to a pressing demand for more sophisticated diagnosis techniques. Clinical assessments and symptomatic analyses for traditional diagnostics suffer from subjectivity, delayed diagnosis, and specificity deficiencies. Therefore, this study developed the Psychological Disorders Machine Learning Genomic (PDMLG) model as an amalgamation of genetic algorithms and machine learning techniques in a predictive analysis model using genomic data samples. The two central components of the PDMLG model include the Genomic Fusion Model, which uses ensemble learning techniques like Random Forest, Gradient Boosting, and Neural Networks, and Deep Learning Model of Convolutional and Recurrent Neural Networks in processing genomic sequence data samples. The model enhanced the disease classification and early detection where the model achieved improvement in precision, recall, and specificity by 3.5% to 9.4% compared to the baseline methods Near Neighbor-Boundary Enlargement (NNBE), Collaborative Mmatrix Factorization based on Correntropy (LDCMFC), and Microsatellite Instability (MSI). The area under the curve of this model is up to 94.95%, which reflects the model’s robust performance on a variety of diseases like Schizophrenia, Bipolar Disorders, and Alzheimer’s. In addition, the PDMLG model can indicate important genetic markers, and this is vital for understanding the genetic basis of psychological conditions that may be diagnosed early and treatment plans prepared in advance for this process. This is a step forward in personalized medicine, which could revolutionize clinical practice in mental disorders diagnostics. This would not be substituted for the established psychological or doctor evaluations. However, it was considered a complementary tool auxiliary for the professional know-how and gives data-related insights that the professional should corroborate for this.