Running Head: SYSTEMS-ANALYTICS-&-ENT-MGMT-IT-7000-FA2-2020-AF-Hafner
IT-related project- Week 1 Final project design SYSTEMS-ANALYTICS-&-ENT-MGMT-IT-7000-FA2-2020-AF-Hafner
Pujitha GANGARAPU (329035)
Smart Health Prediction System
Table of Contents 1. Introduction.......................................................................................................................................3 2. Overview............................................................................................................................................3 3. Steps in Data Mining..........................................................................................................................3 Data Cleaning.....................................................................................................................................3 Data Integration.................................................................................................................................3 Data Selection....................................................................................................................................4 Data Transformation..........................................................................................................................4 Data Mining.......................................................................................................................................4 Pattern Evaluation..............................................................................................................................4 Knowledge representation.................................................................................................................4 4. Techniques in Data Mining.................................................................................................................4 Classification......................................................................................................................................4 Clustering...........................................................................................................................................5 Regression.........................................................................................................................................5 Outlier detection...............................................................................................................................5 Sequential pattern.............................................................................................................................5 Prediction..........................................................................................................................................5 Association Rules...............................................................................................................................5 5. Results...............................................................................................................................................6 6. Conclusion.........................................................................................................................................6 7. References.........................................................................................................................................7
SYSTEMS-ANALYTICS-&-ENT-MGMT-IT-7000-FA2-2020-AF-Hafner
1. Introduction People need expert advice to take care of their health. It doesn't always happen that people go to hospitals to get their tests done and get the reports to know what is their health condition. We need something smarter technology to fill this gaps between human and their health. Sometimes people will not be able to access the use of healthcare facilities because of several factors. The Smart Health Care Prediction System is a project which is the solution for an end-user to all his problems. This project tries to help the user with all his health-related problems and provide services.
2. Overview This platform revolutionizes the manner in which people can monitor and upkeep their health and also enables the patients to reach their nearest health services at the touch of a button. The users get expert guidance from licensed medical practitioners. This online consultation program will have a huge database containing information on myriads of symptoms and possible disease diagnosis for all symptoms. The users had to enter their medical problems and symptoms to get the results. This project uses the method known as 'Data Mining' to find accurate results based on the information received.
3. Steps in Data Mining Data Cleaning Data Cleaning is the first and vital step in this process. This usually clears all the irrelevant data from the actual data.
Data Integration This is the next step after Data Cleaning. This integrates the filtered data from the above step to the meaningful data. SYSTEMS-ANALYTICS-&-ENT-MGMT-IT-7000-FA2-2020-AF-Hafner
Data Selection This step makes sure to select only the relevant data that is used for analysis purpose and retrieves from the collection of data.
Data Transformation This step converts data into forms required to perform different operations such as smoothing, normalization or aggregation.
Data Mining This step examines the data to find the patterns or rules that are useful to be extracted or obtained.
Pattern Evaluation This step identifies the pattern that represents the knowledge based on the given measures.
Knowledge representation This is the last step in this process, and also the result visible to the use. This will make use of data visualization and related techniques and presentations methods to enable the users to make sense of the output of the data mining processes.
4. Techniques in Data Mining There are many techniques in Data Mining to make raw data into a useful one. Below are the techniques from which we can choose or adopt one technique.
Classification This technique involves collecting data and information for analysis and identifying the attributes that SYSTEMS-ANALYTICS-&-ENT-MGMT-IT-7000-FA2-2020-AF-Hafner
define this data and give the result to the user. These attributes further enable the categorization and subsequent management of the data. These attributes further enable the categorization and subsequent management of the data.
Clustering Clustering is a process through which similar (or dissimilar) datasets can be identified and grouped accordingly. It enables a better understanding of data by the users by depicting data in the form of a visual distribution.
Regression This technique involves the identification and analysis of the correlation between the various attributes present in a dataset. Regression is utilized invariably in data modelling to help define the correlation between various attributes used in aspects of data modelling. The relationship may vary depending on its instances.
Outlier detection This is another technique in data mining. This identifies anomalies among the attributes in a data set, i.e. those data items that do not fit a specific definition. It is easy to understand the reasons for any disturbances and take steps to prevent them.
Sequential pattern This technique majorly identifies patterns among the data set for a particular phase. This is useful to detect the mistakes if any, and also in comparing the previous patterns with current ones. The reports are obtained for data happening at regular intervals of time.
Prediction This technique analyses events that happened in the past to predict the future. The historical events which are recorded are useful in this technique to analyze and predict future events.
Association Rules This is another technique in data mining that is taken from the discipline of statistics. It identifies hidden patterns based on common or similar data items present among different data sets. SYSTEMS-ANALYTICS-&-ENT-MGMT-IT-7000-FA2-2020-AF-Hafner
There are many data mining algorithms for healthcare prediction system to consider. Different outcomes will be obtained from the various data mining algorithms available to the user. With this observation, the effectiveness and accuracy of the system are determined.
5. Results The results obtained will contain the disease and its respective accuracy level. The accuracy level is based on various factors such as patient's medical history, age, gender etc. These results are used by clinical doctors to cure the patient's disease. First, the patient needs to login with his Username and Password in the system. Then he enters his health issues and symptoms to detect his disease. The system uses data mining to look into the database and analyze the information given by the user. Then after analyzing it provides the results— the doctors' login to the system by entering their Username and Password. The doctors will be able to see the reports of the patient and help them to cure those diseases. This cycle repeats. This platform provides an opportunity for the patients to interact with the doctors and tell their concerns related to health.
6. Conclusion Smart Health Prediction system is advantageous to both patients and doctors. The patients get treatment for their health condition at any point in time. The doctors can get more patients with this platform. Data Mining plays an important in determining the illness and analyzing the symptoms of the patient. People find a solution to all their health-related problems through this platform.
SYSTEMS-ANALYTICS-&-ENT-MGMT-IT-7000-FA2-2020-AF-Hafner
7. References 20 Exciting Software Development Project Ideas & Topics for Beginners. upGrad blog. (2020). Retrieved 25 October 2020, from https://www.upgrad.com/blog/softwaredevelopment-project-ideas-topics-for-beginners/#19_Smart_health_prediction_system. Smart Health Prediction System with Data Mining. ResearchGate. (2020). Retrieved 25 October 2020, from https://www.researchgate.net/publication/343345506_Smart_Health_Prediction_System _with_Data_Mining. Smart Health Prediction Using Data Mining. Nevon Projects. (2020). Retrieved 25 October 2020, from https://nevonprojects.com/smart-health-prediction-using-data-mining/.