scholarly journals An Algorithm for Predictive Data Mining Approach in Medical Diagnosis

2018 ◽  
Vol 10 (1) ◽  
pp. 11-20 ◽  
Author(s):  
Shakuntala Jatav ◽  
Vivek Sharma
Keyword(s):  
Data Mining ◽  
Medical Diagnosis ◽  
Data Mining Approach ◽  
Predictive Data Mining

Optimal Product Portfolio Formulation by Merging Predictive Data Mining With Multilevel Optimization

2008 ◽  
Vol 130 (4) ◽  
Author(s):  
Conrad S. Tucker ◽  
Harrison M. Kim
Keyword(s):  
Data Mining ◽  
Product Family ◽  
Product Architecture ◽  
Product Portfolio ◽  
Market Demand ◽  
Customer Preference ◽  
Data Mining Approach ◽  
Engineering Level ◽  
Initial Stage ◽  
Predictive Data Mining

This paper addresses two important fundamental areas in product family formulation that have recently begun to receive great attention. First is the incorporation of market demand that we address through a data mining approach where realistic customer preference data are translated into performance design targets. Second is product architecture reconfiguration that we model as a dynamic design entity. The dynamic approach to product architecture optimization differs from conventional static approaches in that a product architecture is not fixed at the initial stage of product design, but rather evolves with fluctuations in customer performance preferences. The benefits of direct customer input in product family design will be realized through the cell phone product family example presented in this work. An optimal family of cell phones is created with modularity decisions made analytically at the engineering level that maximize company profit.

scholarly journals Predicting COVID-19 Incidence Using Data Mining Techniques: A case study of Pakistan

2020 ◽  
Vol 11 (4) ◽  
pp. 168-184
Author(s):  
Saba NOOR ◽  
◽  
Waseem AKRAM ◽  
Touseef AHMED ◽  
Qurat-ul-Ain Qurat-ul-Ain ◽  
...  
Keyword(s):  
Data Mining ◽  
Machine Learning Algorithms ◽  
Wuhan City ◽  
Data Mining Approach ◽  
Life Threatening ◽  
Precautionary Measures ◽  
The Government ◽  
Predictive Data Mining ◽  
Using Data

The Outbreak of Coronavirus (COVID-19) came to the world in early December 2019. The early cases of coronavirus were reported in Wuhan City, Hubei Province, China. Till May 18, 2020, 198 countries have been affected by this life-threatening disease. The most common and known traits of COVID-19 are tiredness, fever, and dry cough. In this paper, we have discussed the Predictive data mining approach for COVID-19 predictions. In Predictive data mining, a model is developed and trained using supervised learning and then it predicts the behavior of provided data. Predictive data mining is a renowned technique known to many health organizations for the classification and prediction of diseases such as Heart disease and various types of cancers etc. There are several factors for comparing the model's accuracy, scalability, and interpretability. This predictive model is compared to the basics of its accuracy. In this proposed approach, we have used WEKA as it provides a vast collection of many machine learning algorithms. The main objective of this paper is to forecast the possible future incidence of corona cases in Pakistan. This study concludes that the number of corona cases will increase swiftly. If the government take proactive steps and strictly implement precautionary measures, then Pakistan may be able to overcome this pandemic.

A Comparative Study on Medical Diagnosis Using Predictive Data Mining

2014 ◽  
pp. 327-360 ◽  
Author(s):  
Seyed Jalaleddin Mousavirad ◽  
Hossein Ebrahimpour-Komleh
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Medical Diagnosis ◽  
Medical Data ◽  
Support Vector ◽  
Medical Data Mining ◽  
Data Mining Techniques ◽  
Predictive Data Mining

Medical diagnosis is a most important problem in medical data mining. The possible errors of a physician can reduce with the help of data mining techniques. The goal of this chapter is to analyze and compare predictive data mining techniques in the medical diagnosis. To this purpose, various data mining techniques such as decision tree, neural networks, support vector machine, and lazy modelling are considered. Results show data mining techniques can considerably help a physician.

A Comparative Study on Medical Diagnosis Using Predictive Data Mining

2016 ◽  
pp. 923-954
Author(s):  
Seyed Jalaleddin Mousavirad ◽  
Hossein Ebrahimpour-Komleh
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Medical Diagnosis ◽  
Medical Data ◽  
Support Vector ◽  
Medical Data Mining ◽  
Data Mining Techniques ◽  
Predictive Data Mining

Medical diagnosis is a most important problem in medical data mining. The possible errors of a physician can reduce with the help of data mining techniques. The goal of this chapter is to analyze and compare predictive data mining techniques in the medical diagnosis. To this purpose, various data mining techniques such as decision tree, neural networks, support vector machine, and lazy modelling are considered. Results show data mining techniques can considerably help a physician.

scholarly journals Implementation of Predictive Data Mining Techniques for Identifying Risk Factors of Early AVF Failure in Hemodialysis Patients

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Mohammad Rezapour ◽  
Morteza Khavanin Zadeh ◽  
Mohammad Mehdi Sepehri
Keyword(s):  
Risk Factors ◽  
Data Mining ◽  
Research Society ◽  
Hemodialysis Patients ◽  
Diabetic Patients ◽  
Prediction Methods ◽  
Early Failure ◽  
Data Mining Approach ◽  
Predictive Data Mining ◽  
Accuracy Rates

Arteriovenous fistula (AVF) is an important vascular access for hemodialysis (HD) treatment but has 20–60% rate of early failure. Detecting association between patient's parameters and early AVF failure is important for reducing its prevalence and relevant costs. Also predicting incidence of this complication in new patients is a beneficial controlling procedure. Patient safety and preservation of early AVF failure is the ultimate goal. Our research society is Hasheminejad Kidney Center (HKC) of Tehran, which is one of Iran's largest renal hospitals. We analyzed data of 193 HD patients using supervised techniques of data mining approach. There were 137 male (70.98%) and 56 female (29.02%) patients introduced into this study. The average of age for all the patients was 53.87 ± 17.47 years. Twenty eight patients had smoked and the number of diabetic patients and nondiabetics was 87 and 106, respectively. A significant relationship was found between “diabetes mellitus,” “smoking,” and “hypertension” with early AVF failure in this study. We have found that these mentioned risk factors have important roles in outcome of vascular surgery, versus other parameters such as “age.” Then we predicted this complication in future AVF surgeries and evaluated our designed prediction methods with accuracy rates of 61.66%–75.13%.

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