Erythemato-squamous diseases (ESD) diagnosis is a significant challenge in dermatology. It is divided into six categories. Artificial intelligence models have been applied to categorize these categories. Artificial intelligent models are black boxes in nature. The objective of this study is to unbox the black-box behavior and interpret the decision-making. Random Forest and XGBoost models are applied on a standard dataset with SHAP value to get interpretability and causability of decision. The Random Forest model had a classification accuracy of 98.21%. Integration of explainability increase the transparency of result and identify the root cause of the disease in the subject. A comprehensive quantitative study will help to adopt artificial intelligence in healthcare with ethical issues like transparency, causability, and interpretability of diagnosis.

The health-related issues have been increased with a wide range in few years. Hence the need for effective and advanced health care systems or aids isexpanding. New methodologies and instruments must be developed to aid the doctors inintelligent health caring of patients. Biomedical signals are a rich source of information, and it is not easy to understand by the normal human beings. To provide ease, extraction and analysis of biomedical signals can help get the correct information to everyone. The signals generated by the brain control the status of the mind and control the action of the whole body. Epilepsy is a disease by which around 50 million people are affected worldwide. Abnormal synchronisation of the neural activity with symptoms like convulsion is the phenomenon of epileptic seizures. An advanced seizure diagnosis system will help in the detection and diagnosis of epileptic seizures. In this paper, clustering algorithms are applied to Electroencephalogram (EEG) data to classify it in normal and epileptic seizures using the Hjorth parameters. After extracting the Hjorth parameters from EEG signals and k-means, basic sequential algorithmic scheme (BSAS), partitioning around medoids (PAM), fuzzy c-means (FCM), and Vally-Seeking clustering algorithms are applied to group it into normal and seizure. With the used dataset, the Vally Seeking clustering algorithm gives the best performance with an accuracy of about 87%.