Volume-4 Issue-6, September 2024 – International Journal of Preventive Medicine and Health (IJPMH)

L109711121224

IJPMH Journal — Tue, 24 Dec 2024 10:04:29 +0000

The International Journal of Preventive Medicine and Health (IJPMH) has ISSN 2582-7588 (online), open-access, peer-reviewed, periodical bi-monthly international journal, which is published by Lattice Science Publication (LSP) in March, May, July, September, November and January. The journal aims to publish high-quality peer–reviewed original articles in the area of Preventive Medicine and Health that covers such as the Nipah Virus, COVID-19(Corona Virus), Henipa Viruses, Antibiotic Resistance, Breastfeeding, Behavioural Health, Clinical Trials, Community Engagement, Community Health, Child/Adolescent Health, Chronic Diseases, Disease Transmission, Health Informatics, Health Care Management, Health Care Quality, Efficiency, Human Nutrition, Immunization, Maternal & Child Health, Primary Healthcare, Public Health, Reproductive Health, Vaccination, Clinical and Epidemiological Diagnosis and Management, Health care, Epidemiology, Biostatistics, Public Health Administration, Health Care Delivery, National Health Problems, Medical Anthropology, Social Medicine. #Nipah Virus #COVID-19(Corona Virus) #Henipa Viruses #Antibiotic Resistance #Breastfeeding #Behavioural Health #Clinical Trials #Community Engagement #Community Health #Child/Adolescent Health #Chronic Diseases #Disease Transmission #Health Informatics #Health Care Management #Health Care Quality, Efficiency #Human Nutrition #Immunization #Maternal & Child Health #Primary Healthcare #Public Health #Reproductive Health #Vaccination #Clinical and Epidemiological Diagnosis and Management #Health Care #Epidemiology #Biostatistics #Public Health Administration #Health Care Delivery #National Health Problems #Medical Anthropology #Social Medicine #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

Tongue Image Diagnosis System using Machine Learning with Hand-Crafted Features
Dushyant V Mankar¹, Pravin S. Chaudhary²

¹Dushyant Mankar, Department of Electronics & Telecommunication, Prof Ram Meghe College of Engineering & Management New Express Way Badnera, Amravati (Maharashtra), India.

²Dr. P.S. Chaudhary, Department of Electronics & Telecommunication, Prof Ram Meghe College of Engineering & Management New Express Way Badnera, Amravati (Maharashtra), India.

Abstract: Traditional Chinese Medicine theorizes a clear relationship between the visual characteristics of the tongue and the operational condition of the body’s organs. The visual characteristics of the tongue can offer important indications for diagnosing diseases. Investigating tongue image processing methods for automated disease identification is a flourishing field of study in the modernization of Traditional Chinese Medicine. Although the autonomous extraction of high-dimensional features is inherently more beneficial in deep learning than in conventional methods, its usefulness in medical image analysis, particularly in tongue images, is limited by the need for extensive training data. This limitation arises from the need for more labelled images. This paper demonstrates the automated diagnosis of tongue photos by analysing digital images using image processing techniques and machine learning, with a focus on major image-based features. The performance simulation and analysis of the suggested system are conducted using MATLAB Software.

Keywords: Tongue Image Classification, Morphological Features, Image Processing, Machine Learning.
Scope of the Article: Health Care Management

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G92340811922

IJPMH Journal — Tue, 24 Dec 2024 09:57:32 +0000

Symptom Based Disease Prediction Using Machine Learning
Ridham Sood¹, Virat Sharma²

¹Ridham Sood, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.

²Virat Sharma, SRM Institute of Science and Technology, Chennai (Tamil Nadu), India.

Abstract: The Disease Prediction Method utilises predictive modelling to forecast a user’s disease based on the symptoms they provide asfeedback to the system. Medical servicesare in dire need of advancement to enable better decisions about patient care and treatment options. In terms of machine learning, Healthcare enables humans to process large and complex medical databases, interpret them, and derive clinical insights. The machine analyses the user’s symptoms as input and returns the likelihood of the disease as output. Implementing the Decision Tree, K-Nearest Neighbour, Naïve Bayes, and Random Forest enables disease prediction. In thispaper, we attempt to integrate machine learning capabilities in healthcare into a single framework. Instead of diagnosis, healthcare can be made smart by implementing disease prediction using machine learning predictive algorithms. Whenan early diagnosis of a disease is not possible, certain cases may arise. As a result, disease prediction can be applied effectively.This paper focuses primarily on creating a scheme, or what we would call an immediate medical provision, that integrates symptoms obtained from multisensory devices, as well as other medical data, and stores it in a healthcare dataset. This dataset will be analysed using a machine learning algorithm with an accuracy of more than 90%.

Keywords: Machine Learning Disease Prediction, Decision K Nearest Neighbor, Naïve Bayes, Random Forest.
Scope of the Article: Health Care Management

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Volume-4 Issue-6, September 2024 – International Journal of Preventive Medicine and Health (IJPMH)

L109711121224

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G92340811922

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