This research paper presents a deep-learning approach to early detection of skin cancer using image augmentation techniques. We introduce a two-stage image augmentation process utilizing geometric augmentation and a generative adversarial network (GAN) to differentiate skin cancer categories. The public HAM10000 dataset was used to test how well the proposed model worked. Various pre-trained convolutional neural network (CNN) models, including Xception, Inceptionv3, Resnet152v2, EfficientnetB7, InceptionresnetV2, and VGG19, were employed. Our approach demonstrates an accuracy of 96.90%, precision of 97.07%, recall of 96.87%, and F1-score of 96.97%, surpassing the performance of other state-of-the-art methods. The paper also discusses the use of Shapley Additive Explanations (SHAP), an interpretable technique for skin cancer diagnosis, which can help clinicians understand the reasoning behind the diagnosis and improve trust in the system. Overall, the proposed method presents a promising approach to automated skin cancer detection that could improve patient outcomes and reduce healthcare costs.

Heart is one of the essential organs that assume a significant part in the human body. However, heart can also cause diseases that affect the death. World Health Organization (WHO) data from 2012 showed that all deaths from cardiovascular disease (vascular) 7.4 million (42.3%) were caused by heart disease. Increased cases of heart disease require a step as an early prevention and prevention efforts by making early diagnosis of heart disease. In this research will be done early diagnosis of heart disease by using data mining process in the form of classification. The algorithm used is K-Nearest Neighbor algorithm with Forward Selection method. The K-Nearest Neighbor algorithm is used for classification in order to obtain a decision result from the diagnosis of heart disease, while the forward selection is used as a feature selection whose purpose is to increase the accuracy value. Forward selection works by removing some attributes that are irrelevant to the classification process. In this research the result of accuracy of heart disease diagnosis with K-Nearest Neighbor algorithm is 73,44%, while result of K-Nearest Neighbor algorithm accuracy with feature selection method 78,66%. It is clear that the incorporation of the K-Nearest Neighbor algorithm with the forward selection method has improved the accuracy result. Keywords - K-Nearest Neighbor, Classification, Heart Disease, Forward Selection, Data Mining

Early detection of skin cancer is crucial for successful treatment and improved patient outcomes. Medical images play a vital role in this process, serving as the primary data source for both traditional and modern diagnostic approaches. This study aims to provide an overview of the significant role of medical images in skin cancer detection and highlight developments in the use of deep learning for early diagnosis. The scope of this survey includes an in-depth exploration of state-of-the-art deep learning methods, an evaluation of public datasets commonly used for training and validation, and a bibliometric analysis of recent advancements in the field. This survey focuses on publications in the Scopus database from 2019 to 2024. The search string is used to find articles by their abstracts, titles, and keywords, and includes several public datasets, like HAM and ISIC, ensuring relevance to the topic. Filters are applied based on the year, document type, source type, and language. The analysis identified 1697 articles, predominantly comprising journal articles and conference proceedings. The analysis shows that the number of articles has increased over the past five years. This growth is driven not only by developed countries but also by developing countries. Dermatology departments in various hospitals play a significant role in advancing skin cancer detection methods. In addition to identifying publication trends, this study also reveals underexplored areas to encourage new explorations using the VOSviewer and Bibliometrix applications.

This research aims to develop a kidney stone object detection system using machine learning techniques like YOLO and object detection, integrated into a Flask-based web interface to support early diagnosis by medical professionals. The trained model demonstrates strong pattern learning capabilities. Evaluation of the public dataset model reveals an average mean Average Precision (mAP) of 0.9698 for 'kidney stone' labels. This detection model exhibits high performance with an accuracy rate of 96.33%, precision of 96.98%, recall of 99.23%, and an F1-score of 98.1%. Clinical data evaluation shows that the YOLOv5-based detection system performs exceptionally well, with an average mAP of 0.9571, accuracy of 93.06%, precision of 95.71%, recall of 97.1%, and F1-score of 96.49%, indicating the model's capability to detect kidney stones with high precision and accuracy. Thus, both the evaluation on the public dataset and clinical dataset performance support accurate diagnosis processes and further treatment planning. Moreover, this research advances to the stage where the detection model can be directly utilized through implementation via Flask web deployment.

Objectives: This study aimed to optimize early coronary heart disease (CHD) prediction using a genetic algorithm (GA)-based convolutional neural network (CNN) feature engineering approach. We sought to overcome the limitations of traditional hyperparameter optimization techniques by leveraging a GA for superior predictive performance in CHD detection.Methods: Utilizing a GA for hyperparameter optimization, we navigated a complex combinatorial space to identify optimal configurations for a CNN model. We also employed information gain for feature selection optimization, transforming the CHD datasets into an image-like input for the CNN architecture. The efficacy of this method was benchmarked against traditional optimization strategies. Results: The advanced GA-based CNN model outperformed traditional methods, achieving a substantial increase in accuracy. The optimized model delivered a promising accuracy range, with a peak of 85% in hyperparameter optimization and 100% accuracy when integrated with machine learning algorithms, namely naïve Bayes, support vector machine, decision tree, logistic regression, and random forest, for both binary and multiclass CHD prediction tasks.Conclusions: The integration of a GA into CNN feature engineering is a powerful technique for improving the accuracy of CHD predictions. This approach results in a high degree of predictive reliability and can significantly contribute to the field of AI-driven healthcare, with the possibility of clinical deployment for early CHD detection. Future work will focus on expanding the approach to encompass a wider set of CHD data and potential integration with wearable technology for continuous health monitoring.

Skizofrenia merupakan gangguan kesehatan jiwa yang menjadi permasalahan masyarakat yang sangat penting serta harus memperoleh perhatian dari pemerintah. Berdasarkan hasil dari Riset Kesehatan Dasar (RisKesDas) pada tahun 2013 di negara Indonesia terdapat 1,7 dari 1000 warga atau kurang lebih 400.000 orang yang menderita penyakit Skizofrenia. Kurang meratanya tenaga kesehatan di bidang kejiwaan memperburuk penanganan yang seharusnya dapat segera dilakukan. Sistem pakar merupakan jawaban yang tepat untuk permasalahan tersebut karena sistem pakar adalah suatu sistem yang dirancang untuk dapat menirukan keahlian seorang pakar atau ahli dalam menjawab pertanyaan dan memecahkan suatu masalah berdasarkan gejala yang diidap oleh pasien. Sistem pakar ini menggunakan metode forward chaining untuk mendapatkan sebuah kesimpulan dari gejala-gejala skizofrenia yang dimiliki oleh pasien dan bayesian network untuk menghitung seberapa akurat suatu sistem pakar tersebut mengidentifikasi suatu masalah. Sistem ini dibangun menggunkan web dengan bahasa pemrogaman PHP serta databsae MySQL untuk menyimpan data skizofrenia. Proses pengujian fungsionalitas sistem pakar ini berjalan dengan baik serta tingkat akurasi tiap-tiap gejala mendapatkan hasil diatas 80%.