# Prototype System for Supporting Medical Diagnosis Based on Voice Interviewing
## 5 Conclusions  
This project aimed to develop a digital system to assist in taking a medical history, which acts as a 'digital doctor'. The system facilitates voice and manual interaction on a smartphone or tablet, simulating a doctor-patient exchange. In this setup, patients describe their symptoms freely, and the system refine the questions, based on patient responses, ultimately providing a ranked list of potential diagnoses. The objective to include a comprehensive set of disease entities was achieved, which would enhance diagnostic accuracy and mitigate the risk of overlooking rare diseases during initial medical assessments. Another objective was to explore decision-making models, particularly neural networks, to identify a configuration that yields 75% accuracy in listing a patient's actual ailment among the top 10 predicted conditions. This target was met, with diagnostic sensitivity at 72.73%, and model stability demonstrated a maximum deviation of 5%, which was verified over 30 tests. The statistical recognition of a disease entity on the basis of a human (specialist doctor) interview is approximately 65%. The results were based on a survey of specialist doctors. A higher target was, therefore, adopted so that the system built would potentially have a higher value in practical application. Our performance evaluation showed the system processed 100 simultaneous medical interviews in 5 min and 49 s on a specified server configuration (4 vCPUs, 8 GB RAM). This aligns with the project milestones, confirming system robustness under realistic workload conditions. In our comparative analysis of classifiers, ExtraTreesClassifier consistently achieved the best performance across all tested disease classes, with a high Balanced Accuracy ranging from 0.96 to 1.0. Its low standard deviation (0.02-0.05) demonstrated both statistical stability and reliability, making it the most promising technique for this application. The results indicate the potential for further enhancements through advanced neural networks and decision-making models. Future research will focus on reducing interpretive errors, especially in understanding the colloquial language used by patients, in order to improve system accuracy. The long-term goal is to make the system publicly accessible to support medical professionals and enhance healthcare quality. The interdisciplinary approach required for the Medical Assistant project, which bridged medicine, computer science, and data engineering, proved essential. The system has demonstrated an increased capability to process and interpret complex medical inquiries, underscoring the potential of digital tools in telemedicine and decision support. This work highlights the advancements in digital health and opens new opportunities for research in telemedicine, where human-machine interaction is becoming integral to patient care. There are plans to extend the functionality with additional modules for advanced speech analysis and more powerful neural networks, which can support the diagnostic process even more effectively. In future, the system should accommodate a wider range of languages and pronunciation variants to increase accessibility for different patient groups. The reliability of user input also remains a significant challenge--inaccurate or overly general descriptions can reduce the accuracy of predictions. In addition, under conditions of high background noise or where patients have pronunciation defects, the system may require additional adjustments or verification. The final diagnosis should always be verified by qualified medical personnel, as the proposed method, despite its high efficiency in testing, cannot completely replace the knowledge and experience of a doctor in clinical practice. Conceptualisation, P.A. and A.S.; methodology, P.A. and A.S.; software, P.A. and A.S.; validation, P.A. and A.S.; formal analysis, P.A. and A.S.; investigation, P.A. and A.S.; resources, P.A. and A.S.; data curation, P.A. and A.S.; writing--original draft preparation, P.A.