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Background. Even in today’s environment, when there is a plethora of information accessible, it may be difficult to make appropriate choices for one’s well-being. Data mining, machine learning, and computational statistics are among the most popular arenas of training today, and they are all aimed at secondary empowered person in making good decisions that will maximize the outcome of whatever working area they are involved with. Because the degree of rise in the number of patient roles is directly related to the rate of people growth and lifestyle variations, the healthcare sector has a significant need for data processing services. When it comes to cancer, the prognosis is an expression that relates to the possibility of the patient surviving in general, but it may also be used to describe the severity of the sickness as it will present itself in the patient's future timeline. Methodology. The proposed technique consists of three stages: input data acquisition, preprocessing, and classification. Data acquisition consists of input raw data which is followed by preprocessing to eliminate the missed data and the classification is carried out using ensemble classifier to analyze the stages of cancer. This study explored the combined influence of the prominent labels in conjunction with one another utilizing the multilabel classifier approach, which is successful. Finally, an ensemble classifier model has been constructed and experimentally validated to increase the accuracy of the classifier model, which has been previously shown. The entire performance of the recommended and tested models demonstrates a steady development of 2% to 6% over the baseline presentation on the baseline performance. Results. Providing a good contribution to the general health welfare of noncommercial potential workers in the healthcare sector is an opportunity provided by this recommended job outcome. It is anticipated that alternative solutions to these constraints, as well as automation of the whole process flow of all five phases, will be the key focus of the work to be carried out shortly. Predicting health status of employee in industry or information trends is made easier by these data patterns. The proposed classifier achieves the accuracy rate of 93.265%.

Every human being has emotion for every item related to them. For every customer, their emotion can help the customer representative to understand their requirement. So, speech emotion recognition plays an important role in the interaction between humans. Now, the intelligent system can help to improve the performance for which we design the convolution neural network (CNN) based network that can classify emotions in different categories like positive, negative, or more specific. In this paper, we use the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) audio records. The Log Mel Spectrogram and Mel-Frequency Cepstral Coefficients (MFCCs) were used to feature the raw audio file. These properties were used in the classification of emotions using techniques, such as Long Short-Term Memory (LSTM), CNNs, Hidden Markov models (HMMs), and Deep Neural Networks (DNNs). For this paper, we have divided the emotions into three sections for males and females. In the first section, we divide the emotion into two classes as positive. In the second section, we divide the emotion into three classes such as positive, negative, and neutral. In the third section, we divide the emotions into 8 different classes such as happy, sad, angry, fearful, surprise, disgust expressions, calm, and fearful emotions. For these three sections, we proposed the model which contains the eight consecutive layers of the 2D convolution neural method. The purposed model gives the better-performed categories to other previously given models. Now, we can identify the emotion of the consumer in better ways.