Imagine you are a detective trying to solve a complex case. You gather evidence from various sources and connect the dots to reveal the truth. In a similar way, scientists conducted a study using back propagation neural network (BPNN) and multilayer perceptron to develop a prediction model for suicide. They collected data through psychological autopsy and used statistical methods to filter factors associated with suicide. Then, they employed neural networks to establish the prediction model. The results showed that the multilayer perceptron model outperformed the BPNN model, achieving an accuracy rate of above 90%. This means that the multilayer perceptron model was able to predict suicide with high precision. These findings have significant implications for clinical diagnosis and the development of artificial intelligence systems to assist in predicting suicide risk. If you’re curious to learn more about this fascinating research, check out the full article!
IntroductionThe aim was to explore the neural network prediction model for suicide based on back propagation (BP) and multilayer perceptron, in order to establish the popular, non-invasive, brief and more precise prediction model of suicide.Materials and methodData were collected by psychological autopsy (PA) in 16 rural counties from three provinces in China. The questionnaire was designed to investigate factors for suicide. Univariate statistical methods were used to preliminary filter factors, and BP neural network and multilayer perceptron were employed to establish the prediction model of suicide.ResultsThe overall percentage correct of samples was 80.9% in logistic regression model. The total coincidence rate for all samples was 82.9% and the area under ROC curve was about 82.0% in the Back Propagation Neural Network (BPNN) prediction model. The AUC of the optimal multilayer perceptron prediction model was above 90% in multilayer perceptron model. The discrimination efficiency of the multilayer perceptron model was superior to BPNN model.ConclusionsThe neural network prediction models have greater accuracy than traditional methods. The multilayer perceptron is the best prediction model of suicide. The neural network prediction model has significance for clinical diagnosis and developing an artificial intelligence (AI) auxiliary clinical system.
Dr. David Lowemann, M.Sc, Ph.D., is a co-founder of the Institute for the Future of Human Potential, where he leads the charge in pioneering Self-Enhancement Science for the Success of Society. With a keen interest in exploring the untapped potential of the human mind, Dr. Lowemann has dedicated his career to pushing the boundaries of human capabilities and understanding.
Armed with a Master of Science degree and a Ph.D. in his field, Dr. Lowemann has consistently been at the forefront of research and innovation, delving into ways to optimize human performance, cognition, and overall well-being. His work at the Institute revolves around a profound commitment to harnessing cutting-edge science and technology to help individuals lead more fulfilling and intelligent lives.
Dr. Lowemann’s influence extends to the educational platform BetterSmarter.me, where he shares his insights, findings, and personal development strategies with a broader audience. His ongoing mission is shaping the way we perceive and leverage the vast capacities of the human mind, offering invaluable contributions to society’s overall success and collective well-being.