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Data classification is considered as a critical and challenging problem to be addressed in IoT medical data analytics. Classification is a method of labeling data for better productive usage depending upon necessity [34]. It functions with two paces: first includes learning activity and the second performs classification activity. The required data can be detected and obtained using well-organized classification model. The action of classifying the data using issues and difficulties opened by the data controllers is called data classification. Figure 1.2 shows the paces connected to big data classification [30]. The different paces connected to classification are input data collection, data understanding, data shaping and data mining environment understanding. The success in data classification requires the understanding about design and structure of algorithms. It demonstrates activities such as configuration of huge data, management of big data and the methodology advancement related to classification [17]. The distinguishing parameters which impact the big data controller management and drive to issues in the advancement of learning layouts. Explores the paces associated with the machine learning algorithms and the flow of various phases is demonstrated. The cross validation and early stopping decision methods are applied for solving problems seen in the validation phase [16].

Figure 1.2 Data mining classification process.

Data classification is the task of applying computer vision and machine learning algorithms to extract meaning from a medical data. This could be as simple as assigning a label to the contents of an image, or data it could be as advanced as interpreting the contents of a data and returning a human-readable sentence [18]. Image and signal classification, at the very core, is the task of assigning a label to a data from a pre-defined set of categories. In practice, this means that given an input image, the task is to analyze the image and return a label that categorizes the image. This label is (almost always) from a pre-defined set. Open-ended classification problems are rarely seen when the list of labels is infinite [2].

In this proposed system examine about checking patient’s mind flags and recognizing the status of the patient progressively. To gather the information of cerebrum signals, we are utilizing Neurosky Mindwave Mobile Headset which deals with the EEG innovation. Figure 1.3 demonstrate the proposed system design for EEG classification. It demonstrates the yield result in waveform design [33]. The overall system is given a multi-channel EEG stream in segments of 3 s every second, and a set of features are extracted at each time point and denoted as a sample. These samples are taken every second such that the subsequent window taken overlaps. As a result, the samples collected show a more gradual transition from one epileptic seizure state to the next [19]. A rectangular window is applied to each 3-second segment such that there is minimal distortion in the frequency response (some distortion will be present due to Gibb’s Phenomenon).

Figure 1.3 Block diagram of the EEG classification.

An FIR signal filter is applied to decompose the incoming EEG stream into its respective brain waves. Features are extracted from the incoming data streams starting from the beginning to the end of the EEG such that it simulates a real-time scenario. If a sample is extracted that contains mathematical anomalies resulting in values of NaN, the sample is simply discarded and skipped over. The training data used is 80% of a new random permutation of the entire training set for every classification performed, and the testing data is the sample that was extracted from the current window [30, 31]. Once the classifiers have each made a prediction, a decision fusion algorithm uses a set of rules to come up with an initial prediction. This prediction is given to the state decision neurons, which use a closed-loop algorithm to determine if a state change is necessary. Table 1.1 shows the various epileptic state of transient EEG cerebrum signals received and stored cloud from IoT-based mindset devices [3, 32].

Table 1.1 Defined epileptic state in transient EEG signal.

State	Epileptic state
1	Postictal state
2	Interictal state
3	Preictal state
4	Ictal state

Since the postictal and interictal states have signal characteristics that are similar (both represent nonictal states), it was necessary to place the states next to each other (i.e. States 1 and 2). This way, if State 2 is misclassified as State 1, or vice versa, then the average of several classifications will also be in the range of States 1 and 2. If these states were defined as States 1 and 4, the average of several classifications would result in increased misclassifications of these states as States 2 or 3, which is incorrect [20].

The reminder of paper is organized as follows. Section 1.2, big data medical dataset prediction and its related work, Section 1.3 discussed about Hybrid Hierarchical clustering feature subsets classifier algorithm, Section 1.4 presents proposed system and existing systems experimental results comparison. Finally, Section 1.5 provides the concluding remarks and future scope of the work.