Journal of Health & Medical Informatics

Abstract Aims: To clarify past history of medical electronics to further promote the studies on medical engineering. Methods: The hand-made medical electronic devices in obstetrics and gynecology were described with their circuits in vacuum tube age, transistor age, integrated circuit (IC) and large scale integration (LSI) ages then customized computer age have been experienced. Results: EEGs were studied to clarify abnormal brain function of eclampsia. The ECG deteccted abnormal cardiac function before gynecologic operations. Fetal diagnosis shifted to fetal heart rate monitoring because of insufficient detection of fetal distress by fetal electro-and phonocardiograms. Simultaneous Doppler fetal movement records and fetal heart rate highly clarified the pathologic process of asphyxia by using actocardiogram. Maternal, fetal and neonatal medicine was highly improved by the electronics devices in every progress stage. Conclusion and recommendation: The physicians and researchers must ask the supply of necessary complicated devices instead of their hand-making at present.

Conventional clinical diagnostic methods are generally based on a single classifier. In present paper, we propose a hybrid Backpropagation neural network (BPNN) classifier based particle swarm optimization (PSO) method. In the present paper by combining the principles two algorithm, we propose a new but simple hybrid algorithm called BPNN_ PSO. Our novel algorithm optimizes BPNN with PSO and reduces computational time of the training phase of BPNN. The performance of the algorithm has been tested with prostate cancer. A total of 360 medical records collected from the patients suffering from neoplasia diseases have been used to train and test the proposed algorithm. The results show that the proposed BPNN–PSO algorithm can achieve very high diagnosis accuracy (98%) and it proving its usefulness in supporting of clinical decision process of prostate cancer. Comparing the simulated results of the above two cases, training the neural network by PSO technique gives more accurate (in terms of sum square error) and also faster (in terms of number of iterations and simulation time) results than BPNN. By using these hybrid method for building machine learning classifiers, we can significantly improve diagnostic performance with respect to the results of clinical practice