Over the past ten years, there has been an increase in cardiovascular disease, one of the most dangerous types of disease. However, cardiovascular detection is a technique that analyzes data and precisely diagnoses ca...Over the past ten years, there has been an increase in cardiovascular disease, one of the most dangerous types of disease. However, cardiovascular detection is a technique that analyzes data and precisely diagnoses cardiovascular disease using machine learning algorithms. Early diagnosis may lead to better outcomes for heart treatment. Then, utilizing machine learning to detect cardiac disease will be easy in a couple of seconds. This study proposes an automatic way for detecting cardiovascular diseases such as heart disease using machine learning. A physician’s accurate and thorough evaluation of a patient’s cardiovascular risk plays a critical role in lowering the incidence and severity of heart attacks and strokes as well as improving cardiovascular protection. To develop technology for the early detection of cardiovascular disease, the Kaggle dataset was gathered. Certain preprocessing techniques were used to improve accuracy and outcomes. Ultimately, we employed decision trees, logistic regression, and random forests to reach our objective. Of these, random forest yielded the highest accuracy of 96%, making them useful for obtaining high-quality results with greater precision.展开更多
One of the most dangerous forms of cancer, skin cancer has been on the rise over the past ten years. Nonetheless, melanoma detection is a method that uses deep learning algorithms to analyze images and accurately diag...One of the most dangerous forms of cancer, skin cancer has been on the rise over the past ten years. Nonetheless, melanoma detection is a method that uses deep learning algorithms to analyze images and accurately diagnose melanoma. An improved result for cancer treatment may result from early diagnosis. Then, in a matter of seconds, it will be simple to identify skin cancer using deep learning. In this research, a deep learning-based automatic skin cancer detection method is proposed. Data was considered from the ISIC database dataset which has 2357 images. To obtain average color information and normalize all color channel information, we used a few preprocessing approaches. Next, data was collected for categorization and reshaping of the images. To avoid overfitting, we additionally employed data augmentation. In the end, the Convolutional Neural Network was used to achieve our goal, which improved the accuracy of prediction. Using the Resnet50 algorithm, the accuracy rate rose to 98%, which will be helpful to get a good outcome with better accuracy.展开更多
文摘Over the past ten years, there has been an increase in cardiovascular disease, one of the most dangerous types of disease. However, cardiovascular detection is a technique that analyzes data and precisely diagnoses cardiovascular disease using machine learning algorithms. Early diagnosis may lead to better outcomes for heart treatment. Then, utilizing machine learning to detect cardiac disease will be easy in a couple of seconds. This study proposes an automatic way for detecting cardiovascular diseases such as heart disease using machine learning. A physician’s accurate and thorough evaluation of a patient’s cardiovascular risk plays a critical role in lowering the incidence and severity of heart attacks and strokes as well as improving cardiovascular protection. To develop technology for the early detection of cardiovascular disease, the Kaggle dataset was gathered. Certain preprocessing techniques were used to improve accuracy and outcomes. Ultimately, we employed decision trees, logistic regression, and random forests to reach our objective. Of these, random forest yielded the highest accuracy of 96%, making them useful for obtaining high-quality results with greater precision.
文摘One of the most dangerous forms of cancer, skin cancer has been on the rise over the past ten years. Nonetheless, melanoma detection is a method that uses deep learning algorithms to analyze images and accurately diagnose melanoma. An improved result for cancer treatment may result from early diagnosis. Then, in a matter of seconds, it will be simple to identify skin cancer using deep learning. In this research, a deep learning-based automatic skin cancer detection method is proposed. Data was considered from the ISIC database dataset which has 2357 images. To obtain average color information and normalize all color channel information, we used a few preprocessing approaches. Next, data was collected for categorization and reshaping of the images. To avoid overfitting, we additionally employed data augmentation. In the end, the Convolutional Neural Network was used to achieve our goal, which improved the accuracy of prediction. Using the Resnet50 algorithm, the accuracy rate rose to 98%, which will be helpful to get a good outcome with better accuracy.
