基于机器学习的疾病预测模型

Monali Gulhane, T. Sajana
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引用次数: 2

摘要

由于人们所处的环境和他们所选择的生活方式,人们现在正遭受各种疾病的折磨。因此,在早期阶段预测疾病的目标变得越来越重要。然而,医生很难根据症状做出准确的预测。准确预测疾病是最困难的任务之一。数据挖掘是克服这一困难的关键,因为它可以用来预测疾病。每年,医学领域都会产生大量的数据。由于卫生和医疗行业收集信息的速度急剧增加,已经有可能对医疗数据进行精确分析,这现在已经导致更好的患者结果。当以疾病数据为出发点时,可以使用数据挖掘来识别当前存在的大量医疗数据中的隐藏模式。根据患者的症状,我们提出了一个通用的疾病预测模型。在实现可靠疾病预测的能力方面,我们应用机器学习方法,如卷积神经网络(cnn)进行疾病预测。疾病症状数据集对于疾病预测至关重要。在这种一般疾病预测模型中,考虑了个体的生活方式行为和检查数据,以进行可靠的疾病预测。研究表明,使用CNN算法进行广义预测建模的准确率为98.7%,确实优于目前的技术。此外,现有机制对时间和内存的要求也高于CNN。当预测到一般疾病时,该方法有资格确定与机构疾病相关的威胁,该威胁可能比前面提到的一般疾病更强或更弱。
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A Machine Learning based Model for Disease Prediction
People are now suffering from a variety of diseases as a result of the environment in which they live and their lifestyle choices. As a result, the goal of predicting disease at an earlier stage becomes increasingly critical. However, making an accurate prediction based on symptoms becomes too tough for doctors to do. The task of accurately predicting disease is one of the most difficult. Data mining is critical in overcoming this difficulty because it may be used to forecast the sickness. Every year, a great amount of data is generated in the field of medicine. Due to the extreme increase in the rate of information being collected in the health and medical industries, it has been possible to conduct precise analyses of medical data, which now has resulted in better patient outcomes. When disease data is used as a starting point, data mining can be used to identify hidden patterns in the huge number of medical data that currently exists. On the basis of the patient's symptoms, we suggested a generic disease prediction model. In ability to implement credible illness predictions, we apply machine learning methods such as convolutional neural networks (CNNs) for disease prediction. Disease symptom datasets are essential for disease forecasting purposes. In this general disease prediction model, the individual's lifestyle behaviour as well as examination data are taken into consideration for reliable disease prediction. It has been demonstrated that the accuracy of generalized predictive modeling that used the CNN algorithm is 98.7 percent, which really is better than those of the present technique. In addition, the time and memory requirements for existing mechanism are higher than those for CNN. When general disease is expected, this method is qualified to determine the threat related to institutional disease, which can be stronger or weaker than the previously mentioned of general disease.
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