Investigation on Nail Denting or Crumbling from Nail Psoriasis Patient Ailment Data using the Machine Learning Algorithm and Data Mining Techniques

Nail denting or crumbling may result from nail psoriasis. A nail condition known as psoriatic onychodystrophy or psoriatic nails. Psoriasis sufferers frequently experience it; reported occurrences range from 10% to 78%. Psoriatic nails are more common in the elderly and in people with psoriatic arthritis. By examining the nails on a human hand, numerous illnesses can be found early in the diagnosing process. The nail color of an individual can help in the diagnosis of specific medical disorders. In this case, the recommended method results in the decision-making process for sickness diagnosis. The system is fed by human nail art. The technique recognizes nail features unique to a given condition by analyzing images of nails. The suggested technique makes advantage of the various distinctive characteristics of human nails to detect illness by changing the colour of the nails. The first training set data, which is derived from an image of a patient’s nails with a particular ailment, is handled by the Weka tool. To get the desired results, the feature discoveries of the picture are compared to the training dataset. The distortion of the nail unit is known as nail disease. Nail units have their own disease class due to their own indications, symptoms, causes, and consequences, which may or may not be related to other medical disorders. Nail problems are still difficult and ambiguous to identify. A novel machine learning method for recognising and classifying nail problems from photographs is presented in this study. This framework combines CNN models with data mining techniques to extract characteristics. This research was also contrasted with certain other province algorithms (Support vector, ANN, K - nearest neighbors, and RF) evaluated on datasets and showed positive results. The accuracy numbers show how well the model can distinguish nail conditions. This ground-breaking discovery lays the groundwork for precise diagnosis in dermatology, medical image processing, and healthcare diagnostics.

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Published
2024-11-06
DOI
https://doi.org/10.1109/iceca63461.2024.10801061
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1
Primary Topic
Systemic Sclerosis and Related Diseases
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article
Field-Weighted Citation Impact
0.42
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article

Investigation on Nail Denting or Crumbling from Nail Psoriasis Patient Ailment Data using the Machine Learning Algorithm and Data Mining Techniques

Krishnamachari Janani, S. Sathya Priya, M. Vijayaraj, S. P. Santhoshkumar et al.
1 citations
Systemic Sclerosis and Related Diseases
0.42
article

Investigation on Nail Denting or Crumbling from Nail Psoriasis Patient Ailment Data using the Machine Learning Algorithm and Data Mining Techniques

Krishnamachari Janani, S. Sathya Priya, M. Vijayaraj, S. P. Santhoshkumar, M. Priyanga, D. Vikram
article en
1 citations

Abstract

Nail denting or crumbling may result from nail psoriasis. A nail condition known as psoriatic onychodystrophy or psoriatic nails. Psoriasis sufferers frequently experience it; reported occurrences range from 10% to 78%. Psoriatic nails are more common in the elderly and in people with psoriatic arthritis. By examining the nails on a human hand, numerous illnesses can be found early in the diagnosing process. The nail color of an individual can help in the diagnosis of specific medical disorders. In this case, the recommended method results in the decision-making process for sickness diagnosis. The system is fed by human nail art. The technique recognizes nail features unique to a given condition by analyzing images of nails. The suggested technique makes advantage of the various distinctive characteristics of human nails to detect illness by changing the colour of the nails. The first training set data, which is derived from an image of a patient’s nails with a particular ailment, is handled by the Weka tool. To get the desired results, the feature discoveries of the picture are compared to the training dataset. The distortion of the nail unit is known as nail disease. Nail units have their own disease class due to their own indications, symptoms, causes, and consequences, which may or may not be related to other medical disorders. Nail problems are still difficult and ambiguous to identify. A novel machine learning method for recognising and classifying nail problems from photographs is presented in this study. This framework combines CNN models with data mining techniques to extract characteristics. This research was also contrasted with certain other province algorithms (Support vector, ANN, K - nearest neighbors, and RF) evaluated on datasets and showed positive results. The accuracy numbers show how well the model can distinguish nail conditions. This ground-breaking discovery lays the groundwork for precise diagnosis in dermatology, medical image processing, and healthcare diagnostics.

Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (IN), Manipal Academy of Higher Education (IN)
Zero hunger
Openalex Percentile: Top 31%
Systemic Sclerosis and Related Diseases
0.42
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