Decision making in medical diagnosis is becoming an important study topic in medical technology. From existing study and understanding in medical science, the primary and minor symptoms of virtually every disease are documented. In the early stages of any sickness, the symptoms of the patient are carefully examined, and by comparing the symptoms, it is possible to deduce the ailment that the patient may be suffering from. Consequently, an effort has been undertaken to implement a fuzzy decision-making strategy based on intuitionistic fuzzy sets. Uncertainty is a factor that makes decision-making more challenging in scientific research. Everyone differs from one another on both a physical and an intellectual level; the indicators of illness revealed by patients are linguistic in character and vary from person to person. For instance, the pain experienced by a patient with a serious brain injury is frequently communicated using linguistic phrases such as a great deal, an excessive lot, acute, etc. A medical professional makes a diagnosis based on the symptoms and signs provided by a patient in order to determine a possible condition. Due to this, scientific diagnosis involves decision-making under uncertainty. In the past few decades, similarity measure has played a significant role in selection technology. Similarity is commonly used to determine the similarity and relationship between two objects. Various similarity measures on intuitionistic fuzzy sets (IFSs) have been developed by numerous scholars and implemented in scientific diagnosis throughout the past few decades. In this paper, we are able to generalize and construct a new similarity measure within the setting of intuitionistic fuzzy sets by employing the fuzzy idea. We provide various examples to support the proposed similarity measures between IFSs. For the performance and reliability of the suggested similarity measure, a comparison examination of numerical instances is conducted. Moreover, the similarity metric is integrated into a multi-criteria decision-making strategy. In addition, we employ our similarity metric between two IFSs to build the intuitionistic fuzzy technique of ordered preference similarity of ideal solution (IF-TOPSIS) method. We use our IF-TOPSIS algorithm to identify mental health issues that involve a complex multicriteria decision-making process. The discussion concludes with a discussion of a medical diagnosis problem in an intuitionistic fuzzy environment.

Keywords: Fuzzy Sets, Intuitionistic Fuzzy Sets, Similarity Measures, Multicriteria Decision Making Problems, Medical Diagnosis, IF-TOPSIS.