## 3 Proposed Energy-Efficient IoT Based Healthcare Model  
The implemented energy-efficient IoT-based healthcare modeling encompasses three most important phases, namely: (i) Energy-efficient clustering (ii) Disease diagnosis (iii) Alert system.  
**Initiation of data and Clustering:** Initially, the medical data, \(D\) are extracted via the Thinkspeak IoT platform and each data are stored in a cloud subsystem. The medical data consists of breast cancer data, heart data and lung cancer data. The nodes or devices are subjected to energy-efficient clustering based on the constraints such as energy and distance. Here, the clustering was conducted with optimal centroid.  
**Disease Diagnosis:** The disease diagnosis was conducted in 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 DCNN, in which weights were optimally tuned by a new hybrid algorithm.  
**Alert System:** Based on the diagnosed disease, the severity level is assessed. This finalizes the need of medical assistants for emergency treatment.  
In this work, optimization algorithms play a key role in efficient disease diagnosis. Particularly, optimal centroid selection during the clustering process and weights of DCNN are also chosen during diagnosis. For this, a new HEM-AL model has been introduced that assists in attaining the defined objective function for optimal training. Figure 1 shows a pictorial representation of the presented IoT-based healthcare framework.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **Adopted Model** & \multicolumn{2}{c}{**Features**} & \multicolumn{1}{c}{**Challenges**} \\ \hline [PERSON] et al. & & & Portrays the & & Need more \\
[1] & Dijkstra algorithm & & aggregate paths & & consideration on \\  & & & Improved accuracy & & practical situations \\  & & & Stable acceptance rate & & Have to focus more \\ [PERSON] et al. [2] & CBR model & & Portrays varied & & on the independent \\  & & & thresholds & & evaluations. \\  & & & More flexibility. & & Needs analysis on the \\ [PERSON] et al. [3] & Fuzzy approach & & Enhanced learning & & applications of \\  & & & and teaching process. & & specific measures. \\  & & & High precision & & \\ [PERSON] [4] & PMF scheme & & Enhanced score & & Network correlation \\  & & & prediction & & has to be considered. \\ [PERSON] et al. [5] & Spearman’s Rank & & Reduced error & & Analysis could be \\  & Correlation & & Highly accurate & & extended to larger \\  & & & Better precision and & & datasets. \\ [PERSON] et al. & & & recall & & Event locations have \\  [6] & KDE technique & & Determines the best & & to be focused more. \\  & & & events. & & \\ [PERSON] et al. [7] & LBM based & & Optimal latency & & Need consideration \\  & method & & Better accuracy on & & on more media \\ [PERSON] et al. [8] & Topic affinity & & prediction & & formats \\  & based model & & Optimal F1-measure & & Computational time \\  & & & Reduced error & & has to be considered. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Reviews on conventional IoT based healthcare systems.