Artificial Intelligence (AI) is widely implemented in healthcare 4.0 for producing early and accurate results. The early predictions of disease help doctors to make early decisions to save the life of patients. Internet of things (IoT) is working as a catalyst to enhance the power of AI applications in healthcare. The patients' data are captured by IoT_sensor and analysis of the patient data is performed by machine learning techniques. The main aim of the work is to propose a Machine learning-based healthcare model to early and accurately predict the different diseases. In this work, seven machine learning classification algorithms such as decision tree, support vector machine, Naïve Bayes, adaptive boosting, Random Forest (RF), artificial neural network, and K-nearest neighbor are used to predict the nine fatal diseases such as heart disease, diabetics breast cancer, hepatitis, liver disorder, dermatology, surgery data, thyroid, and spect heart. To evaluate the performance of the proposed model, four performance metrics (such as accuracy, sensitivity, specificity, and area under the curve) are used. The RF classifier observes the maximum accuracy of 97.62%, the sensitivity of 99.67%, specificity of 97.81%, and AUC of 99.32% for different diseases. The developed healthcare model will help doctors to diagnose the disease early.

人工智能 (AI) 在医疗保健 4.0 中得到广泛应用，以产生早期准确的结果。疾病的早​​期预测有助于医生做出早期决定以挽救患者的生命。物联网 (IoT) 正在发挥催化剂的作用，以增强人工智能在医疗保健领域的应用能力。IoT_sensor 捕获患者数据，并通过机器学习技术对患者数据进行分析。这项工作的主要目的是提出一种基于机器学习的医疗保健模型，以及早准确地预测不同的疾病。在这项工作中，使用决策树、支持向量机、朴素贝叶斯、自适应提升、随机森林（RF）、人工神经网络、K-最近邻等七种机器学习分类算法来预测心脏病等九种致命疾病。疾病，糖尿病患者乳腺癌、肝炎、肝病、皮肤科、手术数据、甲状腺和心脏。为了评估所提出模型的性能，使用了四个性能指标（例如准确性、灵敏度、特异性和曲线下面积）。RF分类器对不同疾病观察到的最大准确率为97.62%，敏感性为99.67%，特异性为97.81%，AUC为99.32%。开发的医疗保健模式将帮助医生及早诊断疾病。81%，不同疾病的 AUC 为 99.32%。开发的医疗保健模式将帮助医生及早诊断疾病。81%，不同疾病的 AUC 为 99.32%。开发的医疗保健模式将帮助医生及早诊断疾病。