Autonomous decision-making: a data mining approach

The researchers and practitioners of today create models, algorithms, functions, and other constructs defined in abstract spaces. The research of the future will likely be data driven. Symbolic and numeric data that are becoming available in large volumes will define the need for new data analysis techniques and tools. Data mining is an emerging area of computational intelligence that offers new theories, techniques, and tools for analysis of large data sets. In this paper, a novel approach for autonomous decision-making is developed based on the rough set theory of data mining. The approach has been tested on a medical data set for patients with lung abnormalities referred to as solitary pulmonary nodules (SPNs). The two independent algorithms developed in this paper either generate an accurate diagnosis or make no decision. The methodology discussed in the paper depart from the developments in data mining as well as current medical literature, thus creating a variable approach for autonomous decision-making.

[1]  K F Hübner,et al.  Differentiating benign from malignant lung lesions using "quantitative" parameters of FDG PET images. , 1996, Clinical nuclear medicine.

[2]  N. Müller,et al.  Solitary pulmonary nodule: high-resolution CT and radiologic-pathologic correlation. , 1991, Radiology.

[3]  Jerzy W. Grzymala-Busse,et al.  A New Version of the Rule Induction System LERS , 1997, Fundam. Informaticae.

[4]  Michael W. Vannier,et al.  Editorial Car Special Issue Of The IEEE Transactions On Information Technology In Biomedicine , 1997, IEEE Trans. Inf. Technol. Biomed..

[5]  T. Y. Lin,et al.  Rough Sets and Data Mining , 1997, Springer US.

[6]  J. Stefanowski,et al.  Rough Sets as a Tool for Studying Attribute Dependencies in the Urinary Stones Treatment Data Set , 1997 .

[7]  Andrew Kusiak,et al.  Computational Intelligence in Design and Manufacturing , 2000 .

[8]  M. Stone.  Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[9]  Taylor Murray,et al.  Cancer statistics, 1999 , 1999, CA: a cancer journal for clinicians.

[10]  G. Klein,et al.  A recognition-primed decision (RPD) model of rapid decision making. , 1993 .

[11]  G. Lillington,et al.  Management of the solitary pulmonary nodule. , 1993, Hospital practice.

[12]  Andrzej Skowron.  Data Filtration: A Rough Set Approach , 1993, RSKD.

[13]  E. Gunel,et al.  Probability of malignancy in solitary pulmonary nodules using fluorine-18-FDG and PET. , 1996, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[14]  S J Swensen,et al.  Solitary pulmonary nodules: clinical prediction model versus physicians. , 1999, Mayo Clinic proceedings.

[15]  Pieter Adriaans,et al.  Data mining , 1996 .

[16]  E A Zerhouni,et al.  The Solitary Pulmonary Nodule: Assessment, Diagnosis, and Management , 1987 .

[17]  Andrzej Skoworon,et al.  Data Filtration: A Rough Set Approach , 1993 .

[18]  Roman Slowinski,et al.  Rough Classification of Patients After Highly Selective Vagotomy for Duodenal Ulcer , 1986, Int. J. Man Mach. Stud..

[19]  Nick Cercone,et al.  Using Rough Sets as Tools for Knowledge Discovery , 1995, KDD.

[20]  Z. Pawlak.  Rough Sets and Data Mining , 2022 .

[21]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[22]  Roman Słowiński,et al.  Rough Classification with Valued Closeness Relation , 1994 .

[23]  Shusaku Tsumoto.  Extraction of Experts' Decision Process from Clinical Databases Using Rough Set Model , 1997, PKDD.

[24]  Frank Slisser,et al.  Modelling Customer Retention with Rough Data Models , 1997, PKDD.

[25]  E. Davis,et al.  The solitary pulmonary nodule. , 1964, The Medical annals of the District of Columbia.