1. Exploring pattern of Alzheimer dataset by using logic mining techniques.
- Author
-
Romli, Nurul Atiqah, Mohd Suhaimi, Ahmad Marwazi, Zamri, Nur Ezlin, Sidik, Siti Syatirah Muhammad, Mohd Kasihmuddin, Mohd Shareduwan, and Wahab, Habibah A.
- Subjects
- *
ALZHEIMER'S disease , *CONSCIOUSNESS raising , *HOPFIELD networks , *NOSOLOGY , *CEREBRAL atrophy - Abstract
In the present, the study on Alzheimer disease is exceptionally important since the number of cases are increasing by almost 10 million every year. According to World Health Organization, Alzheimer disease is one of the major effects of disability and dependency among older people and is currently the seventh leading cause of death among other diseases. There are many experiments conducted by scholars focusing on Alzheimer disease but most of them give little attention to raise awareness on the early detection of Alzheimer disease. Early detection of Alzheimer symptoms can be beneficial to help prevent the development of Alzheimer or slow the progression in people who have symptoms. One of the perspectives that integrate Alzheimer disease in Artificial Intelligence is by using a logic mining model with based Reverse Analysis method that operates in Discrete Hopfield Neural Network. In this paper, a method named Permutation 2 Satisfiability based Reverse Analysis method is implemented to obtain the logical relationship among the factors that contribute towards Alzheimer Disease. The proposed method essentially produces a pattern from analyzing the data and provides an induced logic that represents the possible outcome of the dataset with an average of 95% accuracy. The dataset used in this paper was retrieved from a reputable site of the Alzheimer Disease Neuroimaging Initiative which consists of researchers in the field of dementia science. The investigation shows that several medical factors such as the severity of gross findings in cerebral cortex atrophy, lobar atrophy, and hypopigmentation are highly contributed to the neuropathology in Alzheimer disease classification. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF