Early Detection of Rare Diseases Using Machine Learning

Abstract

The rare diseases, which can be viewed as the diseases that affect only a small percent of the population, create a major issue regarding the proper and timely diagnosis due to low levels of awareness, the lack of medical knowledge, and the availability of data about those patients. Late detection often causes the condition of the patient to deteriorate and increases the cost of treatment. In the context of the fast development of Artificial Intelligence (AI), it is possible to observe that Machine Learning (ML) models become potent sources of enhancing diagnostic accuracy and the speed. This study investigates the possibility of the ML algorithms (including Random Forest, Support Vector Machines (SVM), and Deep Neural Networks) to detect rare diseases early. The research aims at cooperation of heterogeneous data sources such as electronic health records (EHRs), genomic data, medical imaging, and wearable IoT sensor outputs towards training predictive models that can find home in the subtle patterns and can be missed by manual clinicians. The methodology involves data preprocessing to address the imbalanced data to support relevant biomarker discoveries, implementation of feature selection to filter out irrelevant biomarkers and model optimization methods such as Synthetic Minority Oversampling (SMOTE) and hyperparameter tuning. In order to measure performance, evaluation measures that are used include precision, recall, F1-score and ROC-AUC, which ensure clinical reliability. Examples of the application of such software in the real-life instances can be seen on case studies on diseases such as Amyotrophic Lateral Sclerosis (ALS) and Gaucher disease. The results reveal that ML-based methods can help to decrease diagnostic delays drastically, increase the prognosis and assist in developing a personalized treatment approach.