Data Mining and Health: Monitoring Heart Rate for Abnormalities

Category: Data, Statistics
Last Updated: 30 Mar 2023
Pages: 4 Views: 581

This is an accounting calculation, followed by the application of a threshold. However, predicting the profitability of a new customer would be data mining. Dividing the customers off company according to their profitability. Yes, this is a data mining task because it requires data analysis to determine who the costumers are that brings more business to the company. Computing the total sales of the company. No, this is not a data mining task because there Is not analysis involve, this information can be pull out of any booking program. Sorting a student database based on student ID numbers.

No, this Is not a data milling activity because sorting by ID numbers doesn't Involved any data mining task. This is a simple database query Predicting the future stock price of a company using historical records. Yes. We would attempt to create a model that can predict the continuous value of the stock price. This is an example of the area of data mining known as predictive modeling. We could use regression for this modeling, although researchers in many fields have developed a wide variety of techniques for predicting time series. Monitoring the heart rate of a patient for abnormalities. Yes.

We would build a model of the normal behavior of heart rate and raise an alarm when an unusual heart behavior occurred. This would involve the area of data mining known as anomaly detection. This could also be considered as a classification problem If we had examples of both normal and abnormal heart behavior. For each of the following, identify the relevant data mining task(s): The Boston Celtic would like to approximate how many points their next opponent will score against them. A military intelligence officer is interested in learning about the captives proportions of Sunnis and Shies in a particular strategic region. A NORA defense computer must decide immediately whether a blip on the radar is a flick of geese or an incoming nuclear missile. A political strategist is seeking the best groups to canvass for donations in particular county. A homeland security official would like to determine whether a certain sequence of financial and residence moves implies a tendency to terrorist acts. A Wall Street analyst has been asked to find out the expected change in stock price for a set of companies with similar price/earnings ratios.

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Question 3 For each of the following meetings, explain which phase in the CRISP-DIM process is represented: Managers want to know by next week whether deployment will take place. Therefore, analysts meet to discuss how useful and accurate their model is. This is the Evaluation phase in the CRISP-DIM process. In the evaluation phase the data mining analysts determine if the model and technique used meets business objectives established in the first phase. The data mining project manager meets with data warehousing manager to discuss how the data will be collected. This is the

Data Understanding phase in the CRISP-DIM process. The data warehouse is identified as a resource during the Business Understanding phase; however the actual data collection takes place during the Data Understanding Phase. In this phase data is collected and accessed from the resources listed and identified in the Business Understanding phase. The data mining consultant meets with the vice president for marketing, who says that he would like to move forward with customer relationship management. The main objective of business is to review during the Business Understanding Phase.

So, therefore after the meeting it seems the data mining consultant gained success in convincing UP of marketing to provide approval for performing data mining on the customer relationship management system. The data mining project manager meets with the production line supervisor to discuss implementation of changes and improvements. The discussion of implementation of changes and improvements in the project whether specific improvements or process changes are required to ensure that all important aspects of the business are accounted is performed under the Evaluation Phase.

The meeting held with business objective to collect and cleanse the data to ensure the quality of data. The analysts meet to discuss whether the neural network or decision tree model should be applied Question 4 [10 points] Describe the possible negative effects of proceeding directly to mine data that has not been preprocessed. Before data mining algorithms can be used, a target data set must be assembled. As data mining can only uncover patterns actually present in the data, the target data set must be large enough to contain these patterns while imagining concise enough to be mined within an acceptable time limit.

A common source for data is a data mart or data warehouse. Pre-processing is essential to analyze the multivariate data sets before data mining. The target set is then cleaned. Data. Question 5 [1 5 points] Which of the three methods for handling missing values do you prefer? Which method is the most conservative and probably the safest, meaning that it fabricates the least amount of data? What are some drawbacks to this method? Methods for replacing missing field values with: User defined constants Means or modes

Random draws from the distribution of the variable Question 6 Describe the differences between the training set, test set, and validation set. The training set is used to build the model. This contains a set of data that has fricasseed target and predictor variables. Typically a hold-out dataset or test set is used to evaluate how well the model does with data outside the training set. The test set contains the fricasseed results data but they are not used when the test set data is run through the model until the end, when the fricasseed data are compared against the model results.

The model is adjusted to minimize error on the test set. Another hold-out dataset or validation set is used to evaluate the adjusted model in step #2 where, again, the validation set data is run against the adjusted model and results compared to the unused fricasseed data. The training set (seen data) to build the model (determine its parameters) and the test set (unseen data) to measure its performance (holding the parameters constant). Sometimes, we also need a validation set to tune the model (e. G. , for pruning a decision tree). The validation set can't be used for testing (as it's not unseen).

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