Application of Multi-Criteria Decision-Making in Health Care Towards Various Disease Prediction

The healthcare industry presents a challenging situation for decision-making because of its intricate nature and significant impact on people's lives. In previous studies, various models have been proposed to aid healthcare practitioners in predicting various types of diseases. The selection of the best disease prediction model is crucial, as different models may perform inconsistently towards different performance measures. Due to the involvement of many accuracy measures, multi-criteria decision making (MCDM) can be helpful in selecting the best disease prediction model. The objective of this study is many-fold. First, a summary of the various disease prediction models based on machine learning put forth by numerous researchers is provided in this study. Second, this study proposes an MCDM-based framework to evaluate different machine learning techniques (MLTs) used in health care towards various disease predictions. Third, to validate the proposed framework an experimental study was carried out for evaluating different MLTs to predict chronic kidney disease. Results of the experimental study show that the proposed framework can be used efficiently for selecting the most suitable MLT for predicting various types of diseases.