Multi-Disease Prediction based on Symptoms using DL

Aastha Gour
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Abstract

The primary objective of this research is to develop an accurate and efficient model to predict various diseases by analyzing patients' symptoms, thereby enabling early detection, intervention, and personalized treatment plans. Our proposed system employs advanced feature extraction techniques and state-of-the-art Deep Learning (DL) algorithms to analyze and classify symptom patterns, ultimately predicting the most likely diseases associated with the given symptoms. We utilized a comprehensive dataset containing symptom data for numerous diseases, and our system was trained using DL techniques. The performance of the proposed model was evaluated through multiple performance parameters, including accuracy, sensitivity, and specificity. Our experimental results demonstrate the effectiveness and potential of the proposed system in predicting multiple diseases based on symptoms with high accuracy. This study highlights the potential of DL in revolutionizing the field of medical diagnosis and personalized medicine, ultimately improving patient outcomes and healthcare efficiency. The integration of DL with healthcare can bring about a revolution in personalized medicine, and the accurate prediction of diseases can enable early intervention and improve patient outcomes. The article highlights the potential of DL methods in disease prediction and emphasizes the need for further research to overcome the current limitations and challenges. Overall, the article serves as a guide for researchers and healthcare professionals in understanding the role of DL in disease prediction and its implications for the future of healthcare.
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基于症状的多疾病预测
本研究的主要目的是通过分析患者的症状,建立一个准确、高效的模型来预测各种疾病,从而实现早期发现、早期干预和个性化的治疗方案。我们提出的系统采用先进的特征提取技术和最先进的深度学习(DL)算法来分析和分类症状模式,最终预测与给定症状相关的最可能的疾病。我们使用了包含许多疾病症状数据的综合数据集,并且我们的系统使用DL技术进行了训练。该模型的性能通过多个性能参数进行评估,包括准确性、灵敏度和特异性。我们的实验结果证明了该系统在基于症状预测多种疾病方面的有效性和潜力。这项研究强调了深度学习在医疗诊断和个性化医疗领域的革命性潜力,最终改善了患者的治疗效果和医疗效率。DL与医疗保健的整合可以带来个性化医疗的革命,对疾病的准确预测可以实现早期干预并改善患者的预后。文章强调了深度学习方法在疾病预测中的潜力,并强调需要进一步研究以克服当前的局限性和挑战。总的来说,本文为研究人员和医疗保健专业人员理解DL在疾病预测中的作用及其对医疗保健未来的影响提供了指南。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Information Technology in Industry
Information Technology in Industry COMPUTER SCIENCE, SOFTWARE ENGINEERING-
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