###### Abstract  
This paper aims to introduce a novel approach that includes three steps, namely Energy efficient clustering, Disease diagnosis, and an Alert system. Initially, energy-efficient clustering of nodes was conducted, and to render the clustering more optimal, its centroid was optimally selected by a new hybrid algorithm. In addition, this cluster formation was conducted based on constraints such as distance and energy. Further, the disease diagnosis in IoT was performed under two phases namely, \"feature extraction and classification\". During feature extraction, the statistical and higher-order features were extracted. These extracted features were then classified via Optimized Deep Convolutional Neural Network (DCNN). To make the classification more precise, the weights of the DCNN were optimally tuned by a new hybrid algorithm referred to as Hybrid Elephant and Moth Flame with Adaptive Learning (HEM-AL). Finally, an alert system was enabled via proposed severity level estimation, which determined the severity of the disease, suggesting patients to visit the hospital. Lastly, the supremacy of the developed approach was examined via evaluation over the other extant techniques. Accordingly, the proposed model attained an accuracy of 0.99 for test case 1, and was 7.41%, 17.34%, and 13.41% better than traditional NN, CNN, and DCNN models.  
ToT; clustering; disease diagnosis; alert system; HEM-AL algorithm +
Footnote †: journal: Applied Statistics and Applications  
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IoT-based medical appliances collect the required information such as recurrent transformations in health constraints and update the rigorousness of health constraints all within a standardized time interval [7; 8; 9]. In addition, IoT devices and medical parameters related to sensor readings will be deployed for identifying diseases within the correct timeframe before reaching the rigorous state. Machine learning schemes are playing the most important task in making decisions and handling a larger quantity of data [10; 11; 12; 13]. The procedure of adapting data analysis schemes to the definite areas involves defining data types such as volume, variety and velocity [14; 15].  
The benchmark data analysis models include Neural Network (NN), clustering and classification methods in addition to effectual schemes [16; 17; 18; 19]. Data can be produced from a variety of sources with specific data types, and are also significant for developing techniques capable of managing the data features. In IoT, a larger volume of sources produces the essential data without crisis in real time. However, the efficient diagnosis of disease in IoT requires further enhancements in terms of energy efficiency.  
The main contributions of the presented methodology are listed below:  
* Introduction of a new disease diagnosis model in IoT via an energy-efficient optimal clustering approach. Further deep learning-based disease prediction is carried out.
* Introduction of an alert system, which determines the severity of the disease risk level using the correntropy-based method. This insists that the patient requires help from a health care center or hospital for emergency treatment.
* Introduction of a new Hybrid Elephant and Meth Flame with Adaptive Learning for solving optimization issues such as optimal weight selection in Deep Convolutional Neural Network (DCNN) and optimal centroid selection for clustering.  
In this paper, Section 2 describes a review on IoT-based healthcare models. The proposed energy-efficient IoT-based healthcare model is represented in Section 3. Section 4 portrays network creation and energy-efficient clustering. Section 5 depicts disease diagnosis in IoT and Section 6 portrays the alert system: proposed severity level estimation. Section 7 depicts the proposed HEM-AL algorithm for optimal centroid selection and weight optimization. Sections 8 and 9 explain the results and conclusions, respectively.