| Yazarlar (8) |
Vanita Jain
|
|
Bhawanand Jha
|
|
Sourav Joshi
|
|
Sourabh Miglani
|
|
Aman Singal
|
|
Shubham Babbar
|
Dr. Öğr. Üyesi Merve DEMİRCİ
Kafkas Üniversitesi, Türkiye |
|
Müslüm Cengiz Taplamacıoğlu
Gazi Üniversitesi, Türkiye |
| Özet |
| In twentieth century machine learning is being used in various fields; one of the most popular fields among them is the Medical. Few years ago, all the diseases were diagnosed by doctors through expensive machines like X-ray machines, MRI machines and others. Over the last decade disease detection through Machine learning has become quite popular. In this research work, the authors have diagnosed four human diseases viz. Pneumonia, Heart Disease, Breast Cancer and Thyroid. Seven Machine Learning and Deep Learning Algorithms have been used. The accuracies of all Machine Learning models have been compared on different splitting ratio of dataset in order to find the maximum accuracy. The maximum accuracy for heart disease and thyroid by Random Forest is 98.05% and 97.9% respectively. The best result for Breast Cancer by Neural Network is 98.2%. A Hybrid model which consists of Convolutional Neural Network and Support Vector Machine is proposed in this work which gives the maximum accuracy of 97.3% for Pneumonia. Precision, F1-score, Recall have been calculated to compare the results of various Machine Learning and Deep Learning models. Dataset splitting statistics have also been used to compare and evaluate the performance of different Machine Learning algorithms. |
| Anahtar Kelimeler |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SCOPUS dergilerinde yayınlanan tam makale |
| Dergi Adı | International Journal on Technical and Physical Problems of Engineering |
| Dergi ISSN | 2077-3528 Scopus Dergi |
| Dergi Tarandığı Indeksler | Scopus |
| Makale Dili | İngilizce |
| Basım Tarihi | 06-2023 |
| Cilt No | 15 |
| Sayı | 2 |
| Sayfalar | 125 / 133 |
| Makale Linki | https://www.scopus.com/record/display.uri?eid=2-s2.0-85164024133&origin=resultslist&sort=plf-f&src=s&sid=4cfce998edcdafb77dc6744c587ac98a&sot=a&sdt=a&s=SOURCE-ID+%2821100913338%29&sl=23&sessionSearchId=4cfce998edcd |
