التفاصيل البيبلوغرافية
| العنوان: |
Prognostic Evaluation, Prediction and Regimen of Diseases Using SVM, NB and RF Classifiers. |
| المؤلفون: |
Deore, Shalaka, Khuniya, Arpan, Sasi, Ashwin, Pawar, Jagruti, Patel, Mohammed Zeeshan |
| المصدر: |
Revue d'Intelligence Artificielle; Apr2024, Vol. 38 Issue 2, p473-481, 9p |
| مصطلحات موضوعية: |
MACHINE learning, SUPPORT vector machines, RANDOM forest algorithms, DIABETES, PROGRESSION-free survival, DATA mining |
| مصطلحات جغرافية: |
INDIA |
| مستخلص: |
Early diagnosis and prognosis of deadly illnesses have been made possible by advancements in machine learning algorithms. In order to analyze patient pathology reports and make informed decisions regarding medicine supply and marketing strategies, pharmaceutical companies employ advanced data mining tools to generate statistical reports and extract valuable information. Our proposed system fulfils the requirement of patients as well as pharmaceutical clients that are concerned with the two diseases: diabetes mellitus and hypothyroidism. Generating statistical reports from the relevant data and providing an aerial view of the occurrence and spread of a disease in India. The diabetes mellitus and hypothyroidism prediction is carried out using three models: Support Vector Machine (SVM), Naive Bayes, and Random Forest (RF). The Random Forest model is the most appropriate for predicting diabetes mellitus with an accuracy of 90% and 98.05% for predicting hypothyroidism. Diabetes increases a patient's risk of heart disease, stroke and vision problems. Hence our findings help patients to take proactive care. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
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